LightGBM

from lightgbm import LGBMRegressor

model = LGBMRegressor()

params = model.get_params()

# in place

params["objective"] = "regression"

params["learning_rate"] = 0.05

params["n_estimators"] = 1000

params["num_leaves"] = 150

params["metric"] = "mape"

# or

params = {"num_leaves": 150}

model = LGBMRegressor(**params)

model.fit(X_train, y_train, eval_set=(X_test, y_test))

model.predict(X_train, pred_contrib=True)

Save model (use pickle)

model.booster_.save_model('model.txt')

from lightgbm import LGBMClassifier

model = LGBMClassifier(n_jobs=1, silent=False, force_col_wise=True)

model.fit(X_train, y_train)

model.score(X_train, y_train)