class_docstr: The random forest model can be used as a classifier for predictive
  tasks.
  
  The prediction is based on a collection of base learners i.e
  `decision tree classifiers <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 like logistic regression or SVM, gradient
  boosted trees 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.random_forest_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: ["The","random","forest","model","can","be","used","as","a","classifier","for","predictive","tasks",".","The","prediction","is","based","on","a","collection","of","base","learners","i.e","`","decision","tree","classifiers","<","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","like","logistic","regression","or","SVM",",","gradient","boosted","trees","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.random_forest_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: Return a classification, for each example in the ``dataset``, using the
  trained random forest model. The output SFrame contains predictions
  as class labels (0 or 1) and probabilities associated with the the example.
  
  Parameters
  ----------
  dataset : SFrame
      Dataset of new observations. Must include columns with the same
      names as the features used for model training, but does not require
      a target column. Additional columns are ignored.
  
  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 : SFrame
      An SFrame with model predictions i.e class labels and probabilities
      associated with each of the class labels.
  
  See Also
  ----------
  create, evaluate, predict
  
  Examples
  ----------
  >>> data =  turicreate.SFrame('https://static.turi.com/datasets/regression/houses.csv')
  
  >>> data['is_expensive'] = data['price'] > 30000
  >>> model = turicreate.random_forest_classifier.create(data,
  >>>                                                  target='is_expensive',
  >>>                                                  features=['bath', 'bedroom', 'size'])
  >>> classes = model.classify(data)
docstr_tok: ["Return","a","classification",",","for","each","example","in","the","``","dataset","``",",","using","the","trained","random","forest","model",".","The","output","SFrame","contains","predictions","as","class","labels","(","0","or","1",")","and","probabilities","associated","with","the","the","example",".","Parameters","--","--","--","--","--","dataset",":","SFrame","Dataset","of","new","observations",".","Must","include","columns","with","the","same","names","as","the","features","used","for","model","training",",","but","does","not","require","a","target","column",".","Additional","columns","are","ignored",".","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",":","SFrame","An","SFrame","with","model","predictions","i.e","class","labels","and","probabilities","associated","with","each","of","the","class","labels",".","See","Also","--","--","--","--","--","create",",","evaluate",",","predict","Examples","--","--","--","--","--",">",">",">","data","=","turicreate.SFrame","(","'https",":","\/\/static.turi.com\/datasets\/regression\/houses.csv","'",")",">",">",">","data","[","'is_expensive","'","]","=","data","[","'price","'","]",">","30000",">",">",">","model","=","turicreate.random_forest_classifier.create","(","data",",",">",">",">","target='is_expensive","'",",",">",">",">","features=","[","'bath","'",",","'bedroom","'",",","'size","'","]",")",">",">",">","classes","=","model.classify","(","data",")"]
code: def classify(self, dataset, missing_value_action="auto"):
      """
      Return a classification, for each example in the ``dataset``, using the
      trained random forest model. The output SFrame contains predictions
      as class labels (0 or 1) and probabilities associated with the the example.
  
      Parameters
      ----------
      dataset : SFrame
          Dataset of new observations. Must include columns with the same
          names as the features used for model training, but does not require
          a target column. Additional columns are ignored.
  
      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 : SFrame
          An SFrame with model predictions i.e class labels and probabilities
          associated with each of the class labels.
  
      See Also
      ----------
      create, evaluate, predict
  
      Examples
      ----------
      >>> data =  turicreate.SFrame('https://static.turi.com/datasets/regression/houses.csv')
  
      >>> data['is_expensive'] = data['price'] > 30000
      >>> model = turicreate.random_forest_classifier.create(data,
      >>>                                                  target='is_expensive',
      >>>                                                  features=['bath', 'bedroom', 'size'])
      >>> classes = model.classify(data)
      """
      return super(RandomForestClassifier, self).classify(
          dataset, missing_value_action=missing_value_action
      )
  
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 _check_categorical_option_type
  from turicreate.toolkits._tree_model_mixin import TreeModelMixin
  from turicreate.util import _make_internal_url
label_desc: Use image node_id 9 for calling the RandomForestClassifier obj's underlying member method code with example usage: obj.classify(dataset, missing_value_action) and returns: super