def classify(self, dataset, missing_value_action='auto'):
        """
        Return a classification, for each example in the ``dataset``, using the
        trained logistic regression model. The output SFrame contains predictions
        as both class labels (0 or 1) 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.

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

            - 'auto': Default to 'impute'
            - '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 : SFrame
            An SFrame with model predictions i.e class labels and probabilities.

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

        Examples
        ----------
        >>> data =  turicreate.SFrame('https://static.turi.com/datasets/regression/houses.csv')

        >>> data['is_expensive'] = data['price'] > 30000
        >>> model = turicreate.logistic_classifier.create(data,
                                             target='is_expensive',
                                             features=['bath', 'bedroom', 'size'])

        >>> classes = model.classify(data)

        """

        return super(LogisticClassifier, self).classify(dataset,
                                                        missing_value_action=missing_value_action)