code: def predict(self, dataset, missing_value_action='auto'):
          
          return super(RandomForestRegression, self).predict(dataset,
                                                             output_type='margin',
                                                             missing_value_action=missing_value_action)
docs: Predict the target column of the given dataset.
  
          The target column is provided during
          :func:`~turicreate.random_forest_regression.create`. If the target column is in the
          `dataset` it will be ignored.
  
          Parameters
          ----------
          dataset : SFrame
            A dataset that has the same columns that were used during training.
            If the target column exists in ``dataset`` it will be ignored
            while making predictions.
  
          missing_value_action : str, optional
              Action to perform when missing values are encountered. Can be
              one of:
  
              - 'auto': By default the model will treat missing value as is.
              - 'impute': Proceed with evaluation by filling in the missing
                values with the mean of the training data. Missing
                values are also imputed if an entire column of data is
                missing during evaluation.
              - 'error': Do not proceed with evaluation and terminate with
                an error message.
  
          Returns
          -------
          out : SArray
             Predicted target value for each example (i.e. row) in the dataset.
  
          See Also
          ----------
          create, predict
  
          Examples
          --------
          >>> m.predict(testdata)