StratifiedShuffleSplit: ValueError: The Least Populated Class In Y Has Only 1 Member, Which Is Too Few.

- 1 answer

I'm using the StratifiedShuffleSplit cross validator for predicting the house prices in the Boston dataset. When I run the below sample code.

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]

I get the below error. The code works with ShuffleSplit.Does this mean that StratifiedShuffleSplit cannot be used with numeric labels.

ValueError                                Traceback (most recent call last)
<ipython-input-141-b290147edcbf> in <module>()
     33 dt_steps = [('decision', clf)]
---> 35 fit_model_S(labels, features,dt_steps,clf,parameters4)  

<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):
     12         labels_train, labels_test = labels[train_index], labels[test_index]

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\ 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

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\ 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.

Dataset sample as below.

0  6.575   4.98     15.3  504000.0
1  6.421   9.14     17.8  453600.0
2  7.185   4.03     17.8  728700.0
3  6.998   2.94     18.7  701400.0
4  7.147   5.33     18.7  760200.0

The MEDV is the label in this case.



Boston Housing data is a dataset for regression problem. You are using StratifiedShuffleSplit to divide it into train and test. StratifiedShuffleSplit as mentioned in docs is:

This cross-validation object is a merge of StratifiedKFold and ShuffleSplit, which returns stratified randomized folds. The folds are made by preserving the percentage of samples for each class.

Please look at the last line :- "preserving the percentage of samples for each class". So the StratifiedShuffleSplit tries to see the y values as individual classes.

But it will not be possible because your y is a regression variable (continuous numerical data).

Please look at ShuffleSplit, or train_test_split to divide your data. See here for more details on cross-validation: