parameters: (self, X, y=None, groups=None)
docstring: Generate indices to split data into training and test set.
  
          Parameters
          ----------
          X : array-like, shape (n_samples, n_features)
              Training data, where n_samples is the number of samples
              and n_features is the number of features.
  
          y : array-like, shape (n_samples,)
              The target variable for supervised learning problems.
  
          groups : array-like, with shape (n_samples,), optional
              Group labels for the samples used while splitting the dataset into
              train/test set.
  
          Returns
          -------
          train : ndarray
              The training set indices for that split.
  
          test : ndarray
              The testing set indices for that split.
docstring_summary: Generate indices to split data into training and test set.
docstring_tokens: Generate indices to split data into training and test set .
function: def split(self, X, y=None, groups=None):
          """Generate indices to split data into training and test set.
  
          Parameters
          ----------
          X : array-like, shape (n_samples, n_features)
              Training data, where n_samples is the number of samples
              and n_features is the number of features.
  
          y : array-like, shape (n_samples,)
              The target variable for supervised learning problems.
  
          groups : array-like, with shape (n_samples,), optional
              Group labels for the samples used while splitting the dataset into
              train/test set.
  
          Returns
          -------
          train : ndarray
              The training set indices for that split.
  
          test : ndarray
              The testing set indices for that split.
          """
          X, y, groups = indexable(X, y, groups)
          for train, test in self._iter_indices(X, y, groups):
              yield train, test
function_tokens: def split ( self , X , y = None , groups = None ) : X , y , groups = indexable ( X , y , groups ) for train , test in self . _iter_indices ( X , y , groups ) : yield train , test