class_docstr: Encapsulates random forest models for regression tasks.
  
  The prediction is based on a collection of base learners, `regression trees
  <http://en.wikipedia.org/wiki/Decision_tree_learning>`_ and combines them
  through a technique called `random forest <http://en.wikipedia.org/wiki/Random_forest>`_.
  
  Different from linear models, e.g. linear regression,
  the random forests are 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.random_forest_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: __init__(self)
docstr_tok: ["__init__","(","self",")"]
code: def __init__(self, proxy):
      """__init__(self)"""
      self.__proxy__ = proxy
      self.__name__ = self.__class__._native_name()
  
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
  import logging
  from turicreate.util import _make_internal_url
label_desc: Use image node_id 1 to create a new RandomForestRegression object from inherited base classes: _SupervisedLearningModel, _TreeModelMixin with example: obj = RandomForestRegression(proxy)