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)

def predict(self, dataset, missing_value_action='auto'):
        """
        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)
        """
        return super(RandomForestRegression, self).predict(dataset,
                                                           output_type='margin',
                                                           missing_value_action=missing_value_action)