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: Evaluate the model by making predictions of target values and comparing
  these to actual values.
  
  Parameters
  ----------
  dataset : SFrame
      Dataset of new observations. Must include columns with the same
      names as the target and features used for model training. Additional
      columns are ignored.
  
  metric : str, optional
      Name of the evaluation metric.  Possible values are:
  
      - 'auto'             : Returns all available metrics.
      - 'accuracy'         : Classification accuracy (micro average).
      - 'auc'              : Area under the ROC curve (macro average)
      - 'precision'        : Precision score (macro average)
      - 'recall'           : Recall score (macro average)
      - 'f1_score'         : F1 score (macro average)
      - 'log_loss'         : Log loss
      - 'confusion_matrix' : An SFrame with counts of possible prediction/true label combinations.
      - 'roc_curve'        : An SFrame containing information needed for an ROC curve
  
      For more flexibility in calculating evaluation metrics, use the
      :class:`~turicreate.evaluation` module.
  
  missing_value_action : str, optional
      Action to perform when missing values are encountered. This can be
      one of:
  
      - 'auto': Default to 'impute'
      - '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 : dict
      Dictionary of evaluation results where the key is the name of the
      evaluation metric (e.g. `accuracy`) and the value is the evaluation
      score.
  
  See Also
  ----------
  create, predict, classify
  
  Examples
  --------
  .. sourcecode:: python
  
    >>> results = model.evaluate(test_data)
    >>> results = model.evaluate(test_data, metric='accuracy')
    >>> results = model.evaluate(test_data, metric='confusion_matrix')
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code: def evaluate(
      self, dataset, metric="auto", missing_value_action="auto"
  ):
      """
      Evaluate the model by making predictions of target values and comparing
      these to actual values.
  
      Parameters
      ----------
      dataset : SFrame
          Dataset of new observations. Must include columns with the same
          names as the target and features used for model training. Additional
          columns are ignored.
  
      metric : str, optional
          Name of the evaluation metric.  Possible values are:
  
          - 'auto'             : Returns all available metrics.
          - 'accuracy'         : Classification accuracy (micro average).
          - 'auc'              : Area under the ROC curve (macro average)
          - 'precision'        : Precision score (macro average)
          - 'recall'           : Recall score (macro average)
          - 'f1_score'         : F1 score (macro average)
          - 'log_loss'         : Log loss
          - 'confusion_matrix' : An SFrame with counts of possible prediction/true label combinations.
          - 'roc_curve'        : An SFrame containing information needed for an ROC curve
  
          For more flexibility in calculating evaluation metrics, use the
          :class:`~turicreate.evaluation` module.
  
      missing_value_action : str, optional
          Action to perform when missing values are encountered. This can be
          one of:
  
          - 'auto': Default to 'impute'
          - '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 : dict
          Dictionary of evaluation results where the key is the name of the
          evaluation metric (e.g. `accuracy`) and the value is the evaluation
          score.
  
      See Also
      ----------
      create, predict, classify
  
      Examples
      --------
      .. sourcecode:: python
  
        >>> results = model.evaluate(test_data)
        >>> results = model.evaluate(test_data, metric='accuracy')
        >>> results = model.evaluate(test_data, metric='confusion_matrix')
  
      """
      _raise_error_evaluation_metric_is_valid(
          metric,
          [
              "auto",
              "accuracy",
              "confusion_matrix",
              "roc_curve",
              "auc",
              "log_loss",
              "precision",
              "recall",
              "f1_score",
          ],
      )
      return super(_Classifier, self).evaluate(
          dataset,
          missing_value_action=missing_value_action,
          metric=metric,
      )
  
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 6 for calling the RandomForestClassifier obj's underlying member method code with example usage: obj.evaluate(dataset, metric, missing_value_action) and returns: super