def evaluate(self, dataset, metric='auto', max_neighbors=10, radius=None):
        """
        Evaluate the model's predictive accuracy. This is done by predicting the
        target class for instances in a new dataset and comparing to known
        target values.

        Parameters
        ----------
        dataset : SFrame
            Dataset of new observations. Must include columns with the same
            names as the 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.

            - 'confusion_matrix': An SFrame with counts of possible
              prediction/true label combinations.

            - 'roc_curve': An SFrame containing information needed for an roc
              curve (binary classification only).

        max_neighbors : int, optional
            Maximum number of neighbors to consider for each point.

        radius : float, optional
            Maximum distance from each point to a neighbor in the reference
            dataset.

        Returns
        -------
        out : dict
            Evaluation results. The dictionary keys are *accuracy* and
            *confusion_matrix* and *roc_curve* (if applicable).

        See also
        --------
        create, predict, predict_topk, classify

        Notes
        -----
        - Because the model randomly breaks ties between predicted classes, the
          results of repeated calls to `evaluate` method may differ.

        Examples
        --------
        >>> sf_train = turicreate.SFrame({'species': ['cat', 'dog', 'fossa', 'dog'],
        ...                             'height': [9, 25, 20, 23],
        ...                             'weight': [13, 28, 33, 22]})
        >>> m = turicreate.nearest_neighbor_classifier.create(sf, target='species')
        >>> ans = m.evaluate(sf_train, max_neighbors=2,
        ...                  metric='confusion_matrix')
        >>> print ans['confusion_matrix']
        +--------------+-----------------+-------+
        | target_label | predicted_label | count |
        +--------------+-----------------+-------+
        |     cat      |       dog       |   1   |
        |     dog      |       dog       |   2   |
        |    fossa     |       dog       |   1   |
        +--------------+-----------------+-------+
        """

        ## Validate the metric name
        _raise_error_evaluation_metric_is_valid(metric,
                    ['auto', 'accuracy', 'confusion_matrix', 'roc_curve'])

        ## Make sure the input dataset has a target column with an appropriate
        #  type.
        target = self.target
        _raise_error_if_column_exists(dataset, target, 'dataset', target)

        if not dataset[target].dtype == str and not dataset[target].dtype == int:
            raise TypeError("The target column of the evaluation dataset must "
                            "contain integers or strings.")

        if self.num_classes != 2:
            if (metric == 'roc_curve') or (metric == ['roc_curve']):
                err_msg  = "Currently, ROC curve is not supported for "
                err_msg += "multi-class classification in this model."
                raise _ToolkitError(err_msg)
            else:
                warn_msg  = "WARNING: Ignoring `roc_curve`. "
                warn_msg += "Not supported for multi-class classification."
                print(warn_msg)

        ## Compute predictions with the input dataset.
        ystar = self.predict(dataset, output_type='class',
                             max_neighbors=max_neighbors, radius=radius)
        ystar_prob = self.predict(dataset, output_type='probability',
                             max_neighbors=max_neighbors, radius=radius)


        ## Compile accuracy metrics
        results = {}

        if metric in ['accuracy', 'auto']:
            results['accuracy'] = _evaluation.accuracy(targets=dataset[target],
                                                          predictions=ystar)

        if metric in ['confusion_matrix', 'auto']:
            results['confusion_matrix'] = \
                _evaluation.confusion_matrix(targets=dataset[target],
                                                predictions=ystar)

        if self.num_classes == 2:
            if metric in ['roc_curve', 'auto']:
                results['roc_curve'] = \
                      _evaluation.roc_curve(targets=dataset[target],
                                               predictions=ystar_prob)
        return results
Evaluate the model's predictive accuracy. This is done by predicting the
        target class for instances in a new dataset and comparing to known
        target values.

        Parameters
        ----------
        dataset : SFrame
            Dataset of new observations. Must include columns with the same
            names as the 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.

            - 'confusion_matrix': An SFrame with counts of possible
              prediction/true label combinations.

            - 'roc_curve': An SFrame containing information needed for an roc
              curve (binary classification only).

        max_neighbors : int, optional
            Maximum number of neighbors to consider for each point.

        radius : float, optional
            Maximum distance from each point to a neighbor in the reference
            dataset.

        Returns
        -------
        out : dict
            Evaluation results. The dictionary keys are *accuracy* and
            *confusion_matrix* and *roc_curve* (if applicable).

        See also
        --------
        create, predict, predict_topk, classify

        Notes
        -----
        - Because the model randomly breaks ties between predicted classes, the
          results of repeated calls to `evaluate` method may differ.

        Examples
        --------
        >>> sf_train = turicreate.SFrame({'species': ['cat', 'dog', 'fossa', 'dog'],
        ...                             'height': [9, 25, 20, 23],
        ...                             'weight': [13, 28, 33, 22]})
        >>> m = turicreate.nearest_neighbor_classifier.create(sf, target='species')
        >>> ans = m.evaluate(sf_train, max_neighbors=2,
        ...                  metric='confusion_matrix')
        >>> print ans['confusion_matrix']
        +--------------+-----------------+-------+
        | target_label | predicted_label | count |
        +--------------+-----------------+-------+
        |     cat      |       dog       |   1   |
        |     dog      |       dog       |   2   |
        |    fossa     |       dog       |   1   |
        +--------------+-----------------+-------+