__init__.py 文件源码

def regression_with_GBR(X_train, y_train, X_test, y_test, parmsFromNormalization, params = {'n_estimators': 500, 'max_depth': 4, 'min_samples_split': 1,
          'learning_rate': 0.01, 'loss': 'ls'}):
        #GradientBoostingRegressor
    gfr = GradientBoostingRegressor(**params)
    gfr.fit(X_train, y_train)
    y_pred_gbr = gfr.predict(X_test)
    print_regression_model_summary("GBR", y_test, y_pred_gbr, parmsFromNormalization)
    print_feature_importance(X_test, y_test,gfr.feature_importances_)

    #cross validation ( not sure this make sense for regression
    #http://scikit-learn.org/stable/modules/cross_validation.html
    #gfr = GradientBoostingRegressor(**params)
    #scores = cross_validation.cross_val_score(gfr, X_train, y_train, cv=5)
    #print("Accuracy: %0.2f (+/- %0.2f)" % (scores.mean(), scores.std() * 2))
    return y_pred_gbr