Evaluate the model by making predictions of target values and comparing
        these to actual values.

        Parameters
        ----------
        dataset : SFrame
            Dataset of new observations. Must include columns with the same
            names as the session_id, target and features used for model training.
            Additional columns are ignored.

        metric : str, optional
            Name of the evaluation metric.  Possible values are:

            - 'auto'             : Returns all available metrics.
            - 'accuracy'         : Classification accuracy (micro average).
            - 'auc'              : Area under the ROC curve (macro average)
            - 'precision'        : Precision score (macro average)
            - 'recall'           : Recall score (macro average)
            - 'f1_score'         : F1 score (macro average)
            - 'log_loss'         : Log loss
            - 'confusion_matrix' : An SFrame with counts of possible
                                   prediction/true label combinations.
            - 'roc_curve'        : An SFrame containing information needed for an
                                   ROC curve

        Returns
        -------
        out : dict
            Dictionary of evaluation results where the key is the name of the
            evaluation metric (e.g. `accuracy`) and the value is the evaluation
            score.

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

        Examples
        ----------
        .. sourcecode:: python

          >>> results = model.evaluate(data)
          >>> print results['accuracy']

def evaluate(self, dataset, metric='auto'):
        """
        Evaluate the model by making predictions of target values and comparing
        these to actual values.

        Parameters
        ----------
        dataset : SFrame
            Dataset of new observations. Must include columns with the same
            names as the session_id, target and features used for model training.
            Additional columns are ignored.

        metric : str, optional
            Name of the evaluation metric.  Possible values are:

            - 'auto'             : Returns all available metrics.
            - 'accuracy'         : Classification accuracy (micro average).
            - 'auc'              : Area under the ROC curve (macro average)
            - 'precision'        : Precision score (macro average)
            - 'recall'           : Recall score (macro average)
            - 'f1_score'         : F1 score (macro average)
            - 'log_loss'         : Log loss
            - 'confusion_matrix' : An SFrame with counts of possible
                                   prediction/true label combinations.
            - 'roc_curve'        : An SFrame containing information needed for an
                                   ROC curve

        Returns
        -------
        out : dict
            Dictionary of evaluation results where the key is the name of the
            evaluation metric (e.g. `accuracy`) and the value is the evaluation
            score.

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

        Examples
        ----------
        .. sourcecode:: python

          >>> results = model.evaluate(data)
          >>> print results['accuracy']
        """

        avail_metrics = ['accuracy', 'auc', 'precision', 'recall',
                         'f1_score', 'log_loss', 'confusion_matrix', 'roc_curve']
        _tkutl._check_categorical_option_type(
            'metric', metric, avail_metrics + ['auto'])

        if metric == 'auto':
            metrics = avail_metrics
        else:
            metrics = [metric]

        probs = self.predict(dataset, output_type='probability_vector')
        classes = self.predict(dataset, output_type='class')

        ret = {}
        if 'accuracy' in metrics:
            ret['accuracy'] = _evaluation.accuracy(dataset[self.target], classes)
        if 'auc' in metrics:
            ret['auc'] = _evaluation.auc(dataset[self.target], probs, index_map=self._target_id_map)
        if 'precision' in metrics:
            ret['precision'] = _evaluation.precision(dataset[self.target], classes)
        if 'recall' in metrics:
            ret['recall'] = _evaluation.recall(dataset[self.target], classes)
        if 'f1_score' in metrics:
            ret['f1_score'] = _evaluation.f1_score(dataset[self.target], classes)
        if 'log_loss' in metrics:
            ret['log_loss'] = _evaluation.log_loss(dataset[self.target], probs, index_map=self._target_id_map)
        if 'confusion_matrix' in metrics:
            ret['confusion_matrix'] = _evaluation.confusion_matrix(dataset[self.target], classes)
        if 'roc_curve' in metrics:
            ret['roc_curve'] = _evaluation.roc_curve(dataset[self.target], probs, index_map=self._target_id_map)

        return ret