我在使用StratifiedShuffleSplit交叉验证器来预测波士顿房价数据集中的房价。当我运行下面的样例代码时：
def fit_model_S(labels, features,step, clf,parameters): cv = StratifiedShuffleSplit(n_splits=2,test_size=0.10, random_state = 42) print (cv) for train_index, test_index in cv.split(features,labels): labels_train, labels_test = labels[train_index], labels[test_index] features_train, features_test = features[train_index], features[test_index]我得到了下面的错误。代码在使用ShuffleSplit时可以正常运行。这是否意味着StratifiedShuffleSplit不能用于数值标签？
---------------------------------------------------------------------------ValueError Traceback (most recent call last)<ipython-input-141-b290147edcbf> in <module>() 33 dt_steps = [('decision', clf)] 34 ---> 35 fit_model_S(labels, features,dt_steps,clf,parameters4) 36 37 <ipython-input-141-b290147edcbf> in fit_model_S(labels, features, step, clf, parameters) 8 cv = StratifiedShuffleSplit(n_splits=2,test_size=0.10, random_state = 42) 9 print (cv)---> 10 for train_index, test_index in cv.split(features,labels): 11 12 labels_train, labels_test = labels[train_index], labels[test_index]C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_split.py in split(self, X, y, groups) 1194 """ 1195 X, y, groups = indexable(X, y, groups)-> 1196 for train, test in self._iter_indices(X, y, groups): 1197 yield train, test 1198 C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_split.py in _iter_indices(self, X, y, groups) 1535 class_counts = np.bincount(y_indices) 1536 if np.min(class_counts) < 2:-> 1537 raise ValueError("The least populated class in y has only 1" 1538 " member, which is too few. The minimum" 1539 " number of groups for any class cannot"ValueError: The least populated class in y has only 1 member, which is too few. The minimum number of groups for any class cannot be less than 2.数据集样例如下：
RM LSTAT PTRATIO MEDV0 6.575 4.98 15.3 504000.01 6.421 9.14 17.8 453600.02 7.185 4.03 17.8 728700.03 6.998 2.94 18.7 701400.04 7.147 5.33 18.7 760200.0在这种情况下，MEDV是标签。
回答：
波士顿房价数据是一个用于回归问题的数据库。你使用StratifiedShuffleSplit来将其划分为训练集和测试集。正如文档中提到的，StratifiedShuffleSplit是：
这种交叉验证对象是StratifiedKFold和ShuffleSplit的结合，它返回分层随机折叠。折叠是通过保持每个类别的样本百分比来创建的。
请注意最后一行：“保持每个类别的样本百分比”。因此，StratifiedShuffleSplit试图将y值视为各个类别。
但这将是不可能的，因为你的y是一个回归变量（连续数值数据）。
请查看ShuffleSplit或train_test_split来划分你的数据。有关交叉验证的更多详细信息，请参见：http://scikit-learn.org/stable/modules/cross_validation.html#cross-validation
