这是我的kfolds代码kf = KFold(class_label.shape[0], n_folds=5, shuffle=True). for train_index, test_index in kf:.print("Train:", train_index, "Validation:",test_index). X_train, X_test = np.array(x)[train_index], np.array(x)[test_index]. y_train, y_test = np.array(class_label)[train_index], np.array(class_label)[test_index]情节应该看起来像这样,但有10条线我想为每一折画一条线,所以总共应该有十条线:test_score = []. train_score = []. for depth in range(20):. clf = DecisionTreeClassifier(max_depth = depth + 1). clf.fit(X_train,y_train). train_score.append(clf.score(X_train,y_train)). test_score.append(clf.score(X_test,y_test)). plt.figure(figsize = (8,8)). plt.plot(range(20),train_score). plt.plot(range(20), test_score). plt.xlabel('Tree Depth'). plt.ylabel('Accuracy'). plt.legend(['Training set','Test set']).
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