code: def evaluate(self, dataset, metric='auto', missing_value_action='auto'):
          
          _raise_error_evaluation_metric_is_valid(
                  metric, ['auto', 'rmse', 'max_error'])
          return super(BoostedTreesRegression, self).evaluate(dataset,
                                   missing_value_action=missing_value_action,
                                   metric=metric)
docs: Evaluate the model on the given dataset.
  
          Parameters
          ----------
          dataset : SFrame
              Dataset in the same format used for training. The columns names and
              types of the dataset must be the same as that used in training.
  
          metric : str, optional
              Name of the evaluation metric.  Can be one of:
  
              - 'auto': Compute all metrics.
              - 'rmse': Rooted mean squared error.
              - 'max_error': Maximum error.
  
          missing_value_action : str, optional
              Action to perform when missing values are encountered. Can be
              one of:
  
              - 'auto': By default the model will treat missing value as is.
              - '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 : dict
              A dictionary containing the evaluation result.
  
          See Also
          ----------
          create, predict
  
          Examples
          --------
          ..sourcecode:: python
  
            >>> results = model.evaluate(test_data, 'rmse')