Return the classification for each examples in the ``dataset``.
        The output SFrame contains predicted class labels and its probability.

        Parameters
        ----------
        dataset : SFrame | SArray | dict
            The audio data to be classified.
            If dataset is an SFrame, it must have a column with the same name as
            the feature used for model training, but does not require a target
            column. Additional columns are ignored.

        verbose : bool, optional
            If True, prints progress updates and model details.

        batch_size : int, optional
            If you are getting memory errors, try decreasing this value. If you
            have a powerful computer, increasing this value may improve performance.

        Returns
        -------
        out : SFrame
            An SFrame with model predictions, both class labels and probabilities.

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

        Examples
        ----------
        >>> classes = model.classify(data)

def classify(self, dataset, verbose=True, batch_size=64):
        """
        Return the classification for each examples in the ``dataset``.
        The output SFrame contains predicted class labels and its probability.

        Parameters
        ----------
        dataset : SFrame | SArray | dict
            The audio data to be classified.
            If dataset is an SFrame, it must have a column with the same name as
            the feature used for model training, but does not require a target
            column. Additional columns are ignored.

        verbose : bool, optional
            If True, prints progress updates and model details.

        batch_size : int, optional
            If you are getting memory errors, try decreasing this value. If you
            have a powerful computer, increasing this value may improve performance.

        Returns
        -------
        out : SFrame
            An SFrame with model predictions, both class labels and probabilities.

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

        Examples
        ----------
        >>> classes = model.classify(data)
        """
        prob_vector = self.predict(dataset, output_type='probability_vector',
                                   verbose=verbose, batch_size=batch_size)
        id_to_label = self._id_to_class_label

        return _tc.SFrame({
            'class': prob_vector.apply(lambda v: id_to_label[_np.argmax(v)]),
            'probability': prob_vector.apply(_np.max)
        })