Return predictions for ``dataset``, using the trained supervised_learning
        model. Predictions are generated as class labels (0 or
        1).

        Parameters
        ----------
        dataset: SFrame
            Dataset of new observations. Must include columns with the same
            names as the features used for model training, but does not require
            a target column. Additional columns are ignored.

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

            - 'auto': Choose model dependent missing value action
            - '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 prediction and terminate with
              an error message.
        Returns
        -------
        out : SFrame
            An SFrame with model predictions.

def classify(self, dataset, missing_value_action='auto'):
        """
        Return predictions for ``dataset``, using the trained supervised_learning
        model. Predictions are generated as class labels (0 or
        1).

        Parameters
        ----------
        dataset: SFrame
            Dataset of new observations. Must include columns with the same
            names as the features used for model training, but does not require
            a target column. Additional columns are ignored.

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

            - 'auto': Choose model dependent missing value action
            - '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 prediction and terminate with
              an error message.
        Returns
        -------
        out : SFrame
            An SFrame with model predictions.
        """
        if (missing_value_action == 'auto'):
            missing_value_action = select_default_missing_value_policy(self, 'classify')

        # Low latency path
        if isinstance(dataset, list):
            return self.__proxy__.fast_classify(dataset, missing_value_action)
        if isinstance(dataset, dict):
            return self.__proxy__.fast_classify([dataset], missing_value_action)

        _raise_error_if_not_sframe(dataset, "dataset")
        return self.__proxy__.classify(dataset, missing_value_action)