使用GridSearchCV寻找最佳参数组合——机器学习工具箱代码

 

 

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# -*- coding: utf-8 -*- import numpy as np from sklearn.feature_extraction import FeatureHasher from sklearn import datasets from sklearn.ensemble import GradientBoostingClassifier from sklearn.neighbors import KNeighborsClassifier import xgboost as xgb from sklearn.model_selection import GridSearchCV from sklearn.model_selection import train_test_split from sklearn import metrics from matplotlib import pyplot as plt from sklearn.ensemble import GradientBoostingClassifier from sklearn.model_selection import GridSearchCV   def 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