def predict(self, dataset, output_type='class', batch_size=64):
        """
        Return predictions for ``dataset``, using the trained logistic
        regression model. Predictions can be generated as class labels,
        probabilities that the target value is True, or margins (i.e. the
        distance of the observations from the hyperplane separating the
        classes). `probability_vector` returns a vector of probabilities by
        each class.

        For each new example in ``dataset``, the margin---also known as the
        linear predictor---is the inner product of the example and the model
        coefficients. The probability is obtained by passing the margin through
        the logistic function. Predicted classes are obtained by thresholding
        the predicted probabilities at 0.5. If you would like to threshold
        predictions at a different probability level, you can use the
        Turi Create evaluation toolkit.

        Parameters
        ----------
        dataset : SFrame | SArray | turicreate.Image
            The images to be classified.
            If dataset is an SFrame, it must have columns with the same names as
            the features used for model training, but does not require a target
            column. Additional columns are ignored.

        output_type : {'probability', 'margin', 'class', 'probability_vector'}, optional
            Form of the predictions which are one of:

            - 'probability': Prediction probability associated with the True
              class (not applicable for multi-class classification)
            - 'probability_vector': Prediction probability associated with each
              class as a vector. The probability of the first class (sorted
              alphanumerically by name of the class in the training set) is in
              position 0 of the vector, the second in position 1 and so on.
            - 'class': Class prediction. For multi-class classification, this
              returns the class with maximum probability.

        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 : SArray
            An SArray with model predictions. If `dataset` is a single image, the
            return value will be a single prediction.

        See Also
        ----------
        create, evaluate, classify

        Examples
        ----------
        >>> probability_predictions = model.predict(data, output_type='probability')
        >>> margin_predictions = model.predict(data, output_type='margin')
        >>> class_predictions = model.predict(data, output_type='class')

        """
        if not isinstance(dataset, (_tc.SFrame, _tc.SArray, _tc.Image)):
            raise TypeError('dataset must be either an SFrame, SArray or turicreate.Image')
        if(batch_size < 1):
            raise ValueError("'batch_size' must be greater than or equal to 1")

        dataset, unpack = self._canonize_input(dataset)

        extracted_features = self._extract_features(dataset, batch_size=batch_size)
        return unpack(self.classifier.predict(extracted_features, output_type=output_type))