我正在使用网格搜索来寻找两个模型的最佳参数。我需要构建一个使用整个数据集的模型和另一个使用减少的数据集的模型（需要保持两个模型的折叠相同）。因此，对于第二个模型，将从用于第一个模型（使用整个数据集）的相同折叠中省略/删除一系列数据点。以下是我的代码：
rkf = RepeatedKFold(n_splits=2, n_repeats=5, random_state=24)rkf_new_indices = []for train_idx, test_idx in rkf.split(x): Model1x_train, Model1x_test = x[train_idx], x[test_idx] Model1y_train, Model1y_test = y[train_idx], y[test_idx] temp_list1 = train_idx.copy() temp_list2 = test_idx.copy() Model2trn_idx = remove_datapoints(temp_list1, out_list) Model2tst_idx = remove_datapoints(temp_list2, out_list) Model2train_idx = list(Model2trn_idx) Model2test_idx = list(Model2tst_idx) rkf_new_indices = np.append(Model2train_idx, Model2test_idx)param_grid = [{'C': [1, 10, 100, 1000], 'kernel': ['linear']}, {'C': [1, 10, 100, 1000], 'gamma': [0.001, 0.0001], 'kernel': ['rbf']},]svr_model = SVR()# 定义使用整个数据集的模型搜索BASE_SVR = GridSearchCV(svr_model, param_grid, scoring='neg_mean_absolute_error', n_jobs=-1, cv=rkf, return_train_score=True)BASE_SVR_grid_results = BASE_SVR.fit(x, y)# 定义使用减少的数据集的模型搜索New_SVR = GridSearchCV(svr_model, param_grid, scoring='neg_mean_absolute_error', n_jobs=-1, cv=rkf_new_indices, return_train_score=True)# ^^^^^^^^^^^^ 引发TypeErrorNew_SVR_grid_results = New_SVR.fit(x, y)对于第二个GridSearch（第19行），我遇到了以下错误：
for train, test in self.cv:> TypeError: cannot unpack non-iterable numpy.int32 object我在cv=rkf_new_indices这里做错了什么？如何修复这个问题？
回答：
当你运行以下代码段时，分割的输出是
rkf_new_indices = []for train_idx, test_idx in rkf.split([8,8,8,8,8,8,8,8,8]): print(train_idx, test_idx) rkf_new_indices = np.append(train_idx, test_idx)[0 1 2 3] [4 5 6 7 8][4 5 6 7 8] [0 1 2 3][2 3 4 7] [0 1 5 6 8][0 1 5 6 8] [2 3 4 7][1 3 7 8] [0 2 4 5 6][0 2 4 5 6] [1 3 7 8][1 4 7 8] [0 2 3 5 6][0 2 3 5 6] [1 4 7 8][1 2 6 7] [0 3 4 5 8][0 3 4 5 8] [1 2 6 7]然而，rkf_new_indices = np.append(train_idx, test_idx)只获取了最后一个实例：
array([0, 3, 4, 5, 8, 1, 2, 6, 7])你可以尝试rkf_new_indices.append((train_idx, test_idx))来获取所有成对的数据：
[(array([0, 1, 2, 3]), array([4, 5, 6, 7, 8])), (array([4, 5, 6, 7, 8]), array([0, 1, 2, 3])), (array([2, 3, 4, 7]), array([0, 1, 5, 6, 8])), (array([0, 1, 5, 6, 8]), array([2, 3, 4, 7])), (array([1, 3, 7, 8]), array([0, 2, 4, 5, 6])), (array([0, 2, 4, 5, 6]), array([1, 3, 7, 8])), (array([1, 4, 7, 8]), array([0, 2, 3, 5, 6])), (array([0, 2, 3, 5, 6]), array([1, 4, 7, 8])), (array([1, 2, 6, 7]), array([0, 3, 4, 5, 8])), (array([0, 3, 4, 5, 8]), array([1, 2, 6, 7]))]