class_docstr: Special case of gradient boosted trees with the number of trees set to 1.
  
  The decision tree model can be used as a classifier for predictive tasks.
  Different from linear models like logistic regression or SVM, this
  algorithm can model non-linear interactions between the features and the
  target. This model is suitable for handling numerical features and
  categorical features with tens of categories but is less suitable for
  highly sparse features (text data), or with categorical variables that
  encode a large number of categories.
  
  This model cannot be constructed directly.  Instead, use
  :func:`turicreate.decision_tree_classifier.create` to create an instance of
  this model.  Additional details on parameter options and code samples are
  available in the documentation for the create function.
  
  See Also
  --------
  create
class_docstr_tok: ["Special","case","of","gradient","boosted","trees","with","the","number","of","trees","set","to","1",".","The","decision","tree","model","can","be","used","as","a","classifier","for","predictive","tasks",".","Different","from","linear","models","like","logistic","regression","or","SVM",",","this","algorithm","can","model","non-linear","interactions","between","the","features","and","the","target",".","This","model","is","suitable","for","handling","numerical","features","and","categorical","features","with","tens","of","categories","but","is","less","suitable","for","highly","sparse","features","(","text","data",")",",","or","with","categorical","variables","that","encode","a","large","number","of","categories",".","This","model","can","not","be","constructed","directly",".","Instead",",","use",":","func",":","`","turicreate.decision_tree_classifier.create","`","to","create","an","instance","of","this","model",".","Additional","details","on","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 Classifier
  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._internal_utils import _raise_error_if_not_sframe
  from turicreate.toolkits._internal_utils import _check_categorical_option_type
  from turicreate.toolkits._tree_model_mixin import TreeModelMixin
label_desc: Use image node_id 4 for calling the DecisionTreeClassifier obj's underlying member method code with example usage: obj.__repr__() and returns: _toolkit_repr_print