class_docstr: Encapsulates gradient boosted trees for regression tasks.
  
  The prediction is based on a collection of base learners, `regression trees
  <http://en.wikipedia.org/wiki/Decision_tree_learning>`_.
  
  
  Different from linear models, e.g. linear regression,
  the gradient boost trees model is able to model non-linear interactions
  between the features and the target using decision trees as the subroutine.
  It is good for handling numerical features and categorical features with
  tens of categories but is less suitable for highly sparse features such as
  text data.
  
  This model cannot be constructed directly.  Instead, use
  :func:`turicreate.boosted_trees_regression.create` to create an instance
  of this model. A detailed list of parameter options and code samples
  are available in the documentation for the create function.
  
  See Also
  --------
  create
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docstr: 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')
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code: def evaluate(
      self, dataset, metric="auto", missing_value_action="auto"
  ):
      """
      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')
  
      """
      _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,
      )
  
imports: from __future__ import print_function
  from __future__ import division
  from __future__ import absolute_import
  from turicreate.toolkits._supervised_learning import SupervisedLearningModel
  import turicreate.toolkits._supervised_learning
  from turicreate.toolkits._internal_utils import _toolkit_repr_print
  from turicreate.toolkits._internal_utils import _raise_error_evaluation_metric_is_valid
  from turicreate.toolkits._tree_model_mixin import TreeModelMixin
  from turicreate.util import _make_internal_url
label_desc: Use image node_id 6 for calling the BoostedTreesRegression obj's underlying member method code with example usage: obj.evaluate(dataset, metric, missing_value_action) and returns: super