parameters: (self, X)
docstring: Predict class probabilities for X.
  
          Parameters
          ----------
          X : array-like of shape = [n_samples, n_features]
              The input samples.
  
          Raises
          ------
          AttributeError
              If the ``loss`` does not support probabilities.
  
          Returns
          -------
          p : array of shape = [n_samples]
              The class probabilities of the input samples. The order of the
              classes corresponds to that in the attribute `classes_`.
docstring_summary: Predict class probabilities for X.
docstring_tokens: Predict class probabilities for X .
function: def predict_proba(self, X):
          """Predict class probabilities for X.
  
          Parameters
          ----------
          X : array-like of shape = [n_samples, n_features]
              The input samples.
  
          Raises
          ------
          AttributeError
              If the ``loss`` does not support probabilities.
  
          Returns
          -------
          p : array of shape = [n_samples]
              The class probabilities of the input samples. The order of the
              classes corresponds to that in the attribute `classes_`.
          """
          score = self.decision_function(X)
          try:
              return self.loss_._score_to_proba(score)
          except NotFittedError:
              raise
          except AttributeError:
              raise AttributeError('loss=%r does not support predict_proba' %
                                   self.loss)
function_tokens: def predict_proba ( self , X ) : score = self . decision_function ( X ) try : return self . loss_ . _score_to_proba ( score ) except NotFittedError : raise except AttributeError : raise AttributeError ( 'loss=%r does not support predict_proba' % self . loss )