class_docstr: The prediction is based on a collection of base learners, `regression trees
  <http://en.wikipedia.org/wiki/Decision_tree_learning>`_. This algorithm is
  a special case for boosted trees regression with number of trees set to 1.
  
  
  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.decision_tree_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
class_docstr_tok: ["The","prediction","is","based","on","a","collection","of","base","learners",",","`","regression","trees","<","http",":","\/\/en.wikipedia.org\/wiki\/Decision_tree_learning",">","`","_",".","This","algorithm","is","a","special","case","for","boosted","trees","regression","with","number","of","trees","set","to","1",".","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","can","not","be","constructed","directly",".","Instead",",","use",":","func",":","`","turicreate.decision_tree_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"]
docstr: Print a string description of the model, when the model name is entered
  in the terminal.
docstr_tok: ["Print","a","string","description","of","the","model",",","when","the","model","name","is","entered","in","the","terminal","."]
code: def __repr__(self):
      """
      Print a string description of the model, when the model name is entered
      in the terminal.
      """
  
      (sections, section_titles) = self._get_summary_struct()
  
      return _toolkit_repr_print(
          self, sections, section_titles, width=30
      )
  
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
label_desc: Use image node_id 5 for calling the DecisionTreeRegression obj's underlying member method code with example usage: obj.__repr__() and returns: _toolkit_repr_print