Return a classification, for each example in the ``dataset``, using the
        trained model. The output SFrame contains predictions as both class
        labels as well as probabilities that the predicted value is the
        associated label.

        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.

        Returns
        -------
        out : SFrame
            An SFrame with model predictions i.e class labels and probabilities.

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

        Examples
        --------
        >>> import turicreate as tc
        >>> dataset = tc.SFrame({'rating': [1, 5], 'text': ['hate it', 'love it']})
        >>> m = tc.text_classifier.create(dataset, 'rating', features=['text'])
        >>> output = m.classify(dataset)

def classify(self, dataset):
        """
        Return a classification, for each example in the ``dataset``, using the
        trained model. The output SFrame contains predictions as both class
        labels as well as probabilities that the predicted value is the
        associated label.

        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.

        Returns
        -------
        out : SFrame
            An SFrame with model predictions i.e class labels and probabilities.

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

        Examples
        --------
        >>> import turicreate as tc
        >>> dataset = tc.SFrame({'rating': [1, 5], 'text': ['hate it', 'love it']})
        >>> m = tc.text_classifier.create(dataset, 'rating', features=['text'])
        >>> output = m.classify(dataset)

        """
        m = self.__proxy__['classifier']
        target = self.__proxy__['target']
        f = _BOW_FEATURE_EXTRACTOR
        return m.classify(f(dataset, target))