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
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docstr: A flexible and advanced prediction API.
  
  The target column is provided during
  :func:`~turicreate.random_forest.create`. If the target column is in the
  `dataset` it will be ignored.
  
  Parameters
  ----------
  dataset : SFrame
    A dataset that has the same columns that were used during training.
    If the target column exists in ``dataset`` it will be ignored
    while making predictions.
  
  output_type : {'probability', 'margin', 'class', 'probability_vector'}, optional.
      Form of the predictions which are one of:
  
      - 'probability': Prediction probability associated with the True
         class (not applicable for multi-class classification)
      - 'margin': Margin associated with the prediction (not applicable
        for multi-class classification)
      - 'probability_vector': Prediction probability associated with each
        class as a vector. The probability of the first class (sorted
        alphanumerically by name of the class in the training set) is in
        position 0 of the vector, the second in position 1 and so on.
      - 'class': Class prediction. For multi-class classification, this
         returns the class with maximum probability.
  
  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 : SArray
     Predicted target value for each example (i.e. row) in the dataset.
  
  See Also
  ----------
  create, evaluate, classify
  
  Examples
  --------
  >>> m.predict(testdata)
  >>> m.predict(testdata, output_type='probability')
  >>> m.predict(testdata, output_type='margin')
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code: def predict(
      self, dataset, output_type="class", missing_value_action="auto"
  ):
      """
      A flexible and advanced prediction API.
  
      The target column is provided during
      :func:`~turicreate.random_forest.create`. If the target column is in the
      `dataset` it will be ignored.
  
      Parameters
      ----------
      dataset : SFrame
        A dataset that has the same columns that were used during training.
        If the target column exists in ``dataset`` it will be ignored
        while making predictions.
  
      output_type : {'probability', 'margin', 'class', 'probability_vector'}, optional.
          Form of the predictions which are one of:
  
          - 'probability': Prediction probability associated with the True
             class (not applicable for multi-class classification)
          - 'margin': Margin associated with the prediction (not applicable
            for multi-class classification)
          - 'probability_vector': Prediction probability associated with each
            class as a vector. The probability of the first class (sorted
            alphanumerically by name of the class in the training set) is in
            position 0 of the vector, the second in position 1 and so on.
          - 'class': Class prediction. For multi-class classification, this
             returns the class with maximum probability.
  
      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 : SArray
         Predicted target value for each example (i.e. row) in the dataset.
  
      See Also
      ----------
      create, evaluate, classify
  
      Examples
      --------
      >>> m.predict(testdata)
      >>> m.predict(testdata, output_type='probability')
      >>> m.predict(testdata, output_type='margin')
      """
      _check_categorical_option_type(
          "output_type",
          output_type,
          ["class", "margin", "probability", "probability_vector"],
      )
      return super(_Classifier, self).predict(
          dataset,
          output_type=output_type,
          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 7 for calling the RandomForestClassifier obj's underlying member method code with example usage: obj.predict(dataset, output_type, missing_value_action) and returns: super