gbm.py 文件源码

def classification():
    # Generate a random binary classification problem.
    X, y = make_classification(n_samples=350, n_features=15, n_informative=10,
                               random_state=1111, n_classes=2,
                               class_sep=1., n_redundant=0)
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.15,
                                                        random_state=1111)

    model = GradientBoostingClassifier(n_estimators=50, max_depth=4,
                                       max_features=8, learning_rate=0.1)
    model.fit(X_train, y_train)
    predictions = model.predict(X_test)
    print(predictions)
    print(predictions.min())
    print(predictions.max())
    print('classification, roc auc score: %s'
          % roc_auc_score(y_test, predictions))