# -*- coding: utf-8 -*-import numpy as npfrom sklearn.feature_extraction import FeatureHasherfrom sklearn import datasetsfrom sklearn.ensemble import GradientBoostingClassifierfrom sklearn.neighbors import KNeighborsClassifierimport xgboost as xgbfrom sklearn.model_selection import GridSearchCVfrom sklearn.model_selection import train_test_splitfrom sklearn import metricsfrom matplotlib import pyplot as pltfrom sklearn.ensemble import GradientBoostingClassifierfrom sklearn.model_selection import GridSearchCVdef report(test_Y, pred_Y):    print("accuracy_score:")    print(metrics.accuracy_score(test_Y, pred_Y))    print("f1_score:")    print(metrics.f1_score(test_Y, pred_Y))    print("recall_score:")    print(metrics.recall_score(test_Y, pred_Y))    print("precision_score:")    print(metrics.precision_score(test_Y, pred_Y))    print("confusion_matrix:")    print(metrics.confusion_matrix(test_Y, pred_Y))    print("AUC:")    print(metrics.roc_auc_score(test_Y, pred_Y))    f_pos, t_pos, thresh = metrics.roc_curve(test_Y, pred_Y)    auc_area = metrics.auc(f_pos, t_pos)    plt.plot(f_pos, t_pos, 'darkorange', lw=2, label='AUC = %.2f' % auc_area)    plt.legend(loc='lower right')    plt.plot([0, 1], [0, 1], color='navy', linestyle='--')    plt.title('ROC')    plt.ylabel('True Pos Rate')    plt.xlabel('False Pos Rate')    plt.show()if __name__== '__main__':    x, y = datasets.make_classification(n_samples=1000, n_features=100,n_redundant=0, random_state = 1)    train_X, test_X, train_Y, test_Y = train_test_split(x,                                                        y,                                                        test_size=0.2,                                                        random_state=66)    #clf = GradientBoostingClassifier(n_estimators=100)    #clf.fit(train_X, train_Y)    #pred_Y = clf.predict(test_X)    #report(test_Y, pred_Y)    scoring= "f1"    parameters ={'n_estimators': range( 50, 200, 25), 'max_depth': range( 2, 10, 2)}    gsearch = GridSearchCV(estimator= GradientBoostingClassifier(), param_grid= parameters, scoring='accuracy', iid= False, cv= 5)     gsearch.fit(x, y)    print("gsearch.best_params_")     print(gsearch.best_params_)     print("gsearch.best_score_")     print(gsearch.best_score_)

 效果：

gsearch.best_params_

{'max_depth': 4, 'n_estimators': 100}

gsearch.best_score_

0.868142228555714