danyashorokh/[Python] Tuning model params (GridSearchCV)

## [Python] Tuning model params (GridSearchCV)
    param_test1 = {'subsample': [0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]}

    gsearch1 = GridSearchCV(estimator = ensemble.GradientBoostingClassifier(n_estimators=600, learning_rate=0.04, max_depth=5,
    max_leaf_nodes=5, random_state=2, min_samples_leaf=1, min_samples_split=300, max_features='sqrt', verbose=1, loss='exponential'),
    param_grid=param_test1, scoring='roc_auc', n_jobs=4, iid=False, cv=5)

    gsearch1.fit(X_train, y_train)

    print(gsearch1.best_params_, gsearch1.best_score_)
	param_test1 = {'subsample': [0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]}

	gsearch1 = GridSearchCV(estimator = ensemble.GradientBoostingClassifier(n_estimators=600, learning_rate=0.04, max_depth=5,
	max_leaf_nodes=5, random_state=2, min_samples_leaf=1, min_samples_split=300, max_features='sqrt', verbose=1, loss='exponential'),
	param_grid=param_test1, scoring='roc_auc', n_jobs=4, iid=False, cv=5)

	gsearch1.fit(X_train, y_train)

	print(gsearch1.best_params_, gsearch1.best_score_)