parameters: (self, X)
return_statement: return self.classes_[Y.argmax(axis=1)]
docstring: Estimate the best class label for each sample in X.
  
          This is implemented as ``argmax(decision_function(X), axis=1)`` which
          will return the label of the class with most votes by estimators
          predicting the outcome of a decision for each possible class pair.
  
          Parameters
          ----------
          X : (sparse) array-like of shape (n_samples, n_features)
              Data.
  
          Returns
          -------
          y : numpy array of shape [n_samples]
              Predicted multi-class targets.
docstring_summary: Estimate the best class label for each sample in X.
docstring_tokens: Estimate the best class label for each sample in X .
function: def predict(self, X):
          """Estimate the best class label for each sample in X.
  
          This is implemented as ``argmax(decision_function(X), axis=1)`` which
          will return the label of the class with most votes by estimators
          predicting the outcome of a decision for each possible class pair.
  
          Parameters
          ----------
          X : (sparse) array-like of shape (n_samples, n_features)
              Data.
  
          Returns
          -------
          y : numpy array of shape [n_samples]
              Predicted multi-class targets.
          """
          Y = self.decision_function(X)
          if self.n_classes_ == 2:
              return self.classes_[(Y > 0).astype(np.int)]
          return self.classes_[Y.argmax(axis=1)]
function_tokens: def predict ( self , X ) : Y = self . decision_function ( X ) if self . n_classes_ == 2 : return self . classes_ [ ( Y > 0 ) . astype ( np . int ) ] return self . classes_ [ Y . argmax ( axis = 1 ) ]