Evaluate the model on the given dataset.


        Parameters
        ----------
        dataset : SFrame
            Dataset in the same format used for training. The columns names and
            types of the dataset must be the same as that used in training.

        metric : str, optional
            Name of the evaluation metric.  Possible values are:
            'auto'      : Compute all metrics.
            'rmse'      : Rooted mean squared error.
            'max_error' : Maximum error.

        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 : dict
            A dictionary containing the evaluation result.

        See Also
        --------
        create, predict

        Examples
        --------
        .. sourcecode:: python

          >>> results = model.evaluate(test_data, 'rmse')

def evaluate(self, dataset, metric='auto', missing_value_action='auto'):
        """
        Evaluate the model on the given dataset.


        Parameters
        ----------
        dataset : SFrame
            Dataset in the same format used for training. The columns names and
            types of the dataset must be the same as that used in training.

        metric : str, optional
            Name of the evaluation metric.  Possible values are:
            'auto'      : Compute all metrics.
            'rmse'      : Rooted mean squared error.
            'max_error' : Maximum error.

        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 : dict
            A dictionary containing the evaluation result.

        See Also
        --------
        create, predict

        Examples
        --------
        .. sourcecode:: python

          >>> results = model.evaluate(test_data, 'rmse')

        """
        _raise_error_evaluation_metric_is_valid(
                metric, ['auto', 'rmse', 'max_error'])
        return super(RandomForestRegression, self).evaluate(dataset,
                                 missing_value_action=missing_value_action,
                                 metric=metric)