我寫(xiě)了一個(gè) KNN 插補(bǔ)實(shí)現(xiàn)，我希望 StratifiedKFold 來(lái)檢查使用什么 K 和什么距離矩陣。我收到一個(gè)錯(cuò)誤：它似乎沒(méi)有將我的估算器識(shí)別為回歸器（“評(píng)分”函數(shù)用于回歸）。我的代碼：skf = StratifiedKFold(n_splits=10, shuffle=False, random_state=12)NN = KnnImputation() # my own functiongridSearchNN = GridSearchCV(NN, param_grid=params, scoring='mean_squared_error', n_jobs=numIter,                            cv=skf.split(xTrain, yTrain), verbose=verbose)gridSearchNN.fit(xTrain, yTrain)錯(cuò)誤：  File "........\dataImputation.py", line 63, in knnImputationMethod    gridSearchNN.fit(xTrain, yTrain)  File "C:\Users\...\Anaconda3\lib\site-packages\sklearn\model_selection\_search.py", line 651, in fit    cv = check_cv(self.cv, y, classifier=is_classifier(estimator))  File "C:\Users\....\Anaconda3\lib\site-packages\sklearn\model_selection\_split.py", line 2068, in check_cv    return _CVIterableWrapper(cv)  File "C:\Users\....\Anaconda3\lib\site-packages\sklearn\model_selection\_split.py", line 1966, in __init__    self.cv = list(cv)  File "C:\Users\...\Anaconda3\lib\site-packages\sklearn\model_selection\_split.py", line 331, in split    for train, test in super(_BaseKFold, self).split(X, y, groups):  File "C:\Users\...\Anaconda3\lib\site-packages\sklearn\model_selection\_split.py", line 100, in split    for test_index in self._iter_test_masks(X, y, groups):  File "C:\Users\...\Anaconda3\lib\site-packages\sklearn\model_selection\_split.py", line 681, in _iter_test_masks    test_folds = self._make_test_folds(X, y)  File "C:\Users\...\Anaconda3\lib\site-packages\sklearn\model_selection\_split.py", line 636, in _make_test_folds    allowed_target_types, type_of_target_y))ValueError: Supported target types are: ('binary', 'multiclass'). Got 'continuous' instead.在“GridSearchCV”過(guò)程中，我看到它進(jìn)入“is_classifier”而不是“is_regressor”。有任何想法嗎？