parameters: (self, X)
docstring: Compute decision function of ``X`` for each iteration.
  
          This method allows monitoring (i.e. determine error on testing set)
          after each stage.
  
          Parameters
          ----------
          X : array-like of shape = [n_samples, n_features]
              The input samples.
  
          Returns
          -------
          score : generator of array, shape = [n_samples, k]
              The decision function of the input samples. The order of the
              classes corresponds to that in the attribute `classes_`.
              Regression and binary classification are special cases with
              ``k == 1``, otherwise ``k==n_classes``.
docstring_summary: Compute decision function of ``X`` for each iteration.
docstring_tokens: Compute decision function of X for each iteration .
function: def staged_decision_function(self, X):
          """Compute decision function of ``X`` for each iteration.
  
          This method allows monitoring (i.e. determine error on testing set)
          after each stage.
  
          Parameters
          ----------
          X : array-like of shape = [n_samples, n_features]
              The input samples.
  
          Returns
          -------
          score : generator of array, shape = [n_samples, k]
              The decision function of the input samples. The order of the
              classes corresponds to that in the attribute `classes_`.
              Regression and binary classification are special cases with
              ``k == 1``, otherwise ``k==n_classes``.
          """
          for dec in self._staged_decision_function(X):
              # no yield from in Python2.X
              yield dec
function_tokens: def staged_decision_function ( self , X ) : for dec in self . _staged_decision_function ( X ) : # no yield from in Python2.X yield dec