這是我的kfolds代碼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]情節(jié)應(yīng)該看起來像這樣，但有10條線我想為每一折畫一條線，所以總共應(yīng)該有十條線：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']).