我正在嘗試使用 SVM 分類器使用自定義交叉驗(yàn)證折疊來建模二元分類問題，但它給了我錯(cuò)誤 **需要至少一個(gè)數(shù)組來連接 ** 與 cross_val_predict。該代碼在 cros_val_predict 中的 cv=3 下工作正常，但是當(dāng)我使用 custom_cv 時(shí)，它會(huì)出現(xiàn)此錯(cuò)誤。下面是代碼：from sklearn.model_selection import LeavePOutimport numpy as npfrom sklearn.svm import SVCfrom time import *from sklearn.metrics import roc_auc_scorefrom sklearn.model_selection import cross_val_predict,cross_val_scoreclf = SVC(kernel='linear',C=25)X = np.array([[1, 2], [3, 4], [5, 6], [7, 8],[9,10]])y = np.array([0,1,1,0,0])lpo = LeavePOut(2)print(lpo.get_n_splits(X))LeavePOut(p=2)test_index_list=[]train_index_list=[]for train_index, test_index in lpo.split(X,y):    if(y[test_index[0]]==y[test_index[1]]):    pass  else:    print("TRAIN:", train_index, "TEST:", test_index)    X_train, X_test = X[train_index], X[test_index]    y_train, y_test = y[train_index], y[test_index]    train_index_list.append(train_index)    test_index_list.append(test_index)custom_cv = zip(train_index_list, test_index_list)scores = cross_val_score(clf, X, y, cv=custom_cv)print(scores)print('accuracy:',scores.mean())predicted=cross_val_predict(clf,X,y,cv=custom_cv) # error with this lineprint('Confusion matrix:',confusion_matrix(labels, predicted))以下是錯(cuò)誤的完整跟蹤：ValueError                                Traceback (most recent call last)<ipython-input-11-d78feac932b2> in <module>()     31 print(scores)     32 print('accuracy:',scores.mean())---> 33 predicted=cross_val_predict(clf,X,y,cv=custom_cv)     34      35 print('Confusion matrix:',confusion_matrix(labels, predicted))關(guān)于如何解決此錯(cuò)誤有什么建議嗎？