OptGBM
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Optuna + LightGBM = OptGBM
OptGBM
OptGBM (= Optuna + LightGBM) provides a scikit-learn compatible estimator that tunes hyperparameters in LightGBM with Optuna.
Examples
import optgbm as lgb
from sklearn.datasets import load_boston
reg = lgb.LGBMRegressor(random_state=0)
X, y = load_boston(return_X_y=True)
reg.fit(X, y)
y_pred = reg.predict(X, y)
By default, the following hyperparameters will be searched.
-
bagging_fraction
-
bagging_freq
-
feature_fractrion
-
lambda_l1
-
lambda_l2
-
max_depth
-
min_data_in_leaf
-
num_leaves
Installation
pip install optgbm
Testing
tox