Evaluate the model by making predictions of target values and comparing
        these to actual values.

        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, list[str]
            Evaluation metric(s) to be computed.

        missing_value_action: str, optional
            Action to perform when missing values are encountered. This can be
            one of:

            - 'auto': Choose a model dependent missing value policy.
            - '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.
            - 'none': Treat missing value as is. Model must be able to handle missing value.
            - 'error' : Do not proceed with prediction and terminate with
                        an error message.

        options : dict
            additional options to be passed in to prediction

        kwargs : dict
            additional options to be passed into prediction

def evaluate(self, dataset, metric="auto",
                 missing_value_action='auto', with_predictions=False, options={}, **kwargs):
        """
        Evaluate the model by making predictions of target values and comparing
        these to actual values.

        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, list[str]
            Evaluation metric(s) to be computed.

        missing_value_action: str, optional
            Action to perform when missing values are encountered. This can be
            one of:

            - 'auto': Choose a model dependent missing value policy.
            - '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.
            - 'none': Treat missing value as is. Model must be able to handle missing value.
            - 'error' : Do not proceed with prediction and terminate with
                        an error message.

        options : dict
            additional options to be passed in to prediction

        kwargs : dict
            additional options to be passed into prediction
        """
        if missing_value_action == 'auto':
            missing_value_action = select_default_missing_value_policy(
                                                             self, 'evaluate')

        _raise_error_if_not_sframe(dataset, "dataset")
        results = self.__proxy__.evaluate(
            dataset, missing_value_action, metric, with_predictions=with_predictions);
        return results