docstr: Evaluate the model by making predictions of target values and comparing
  these to actual values.
  
  Parameters
  ----------
  dataset : SFrame
      An SFrame having the same feature columns as provided when creating
      the model.
  
  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.
  
  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
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code: def evaluate(self, dataset, metric="auto", **kwargs):
      """
      Evaluate the model by making predictions of target values and comparing
      these to actual values.
  
      Parameters
      ----------
      dataset : SFrame
          An SFrame having the same feature columns as provided when creating
          the model.
  
      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.
  
      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
  
      """
      m = self.__proxy__["classifier"]
      target = self.__proxy__["target"]
      f = _BOW_FEATURE_EXTRACTOR
      test = f(dataset, target)
      return m.evaluate(test, metric, **kwargs)
  
imports: from __future__ import print_function
  from __future__ import division
  from __future__ import absolute_import
  import turicreate
  from turicreate.toolkits._internal_utils import _raise_error_if_not_sframe
  from turicreate.toolkits._model import CustomModel
  from turicreate.toolkits._model import PythonProxy
  from turicreate.toolkits._internal_utils import _toolkit_repr_print
  from turicreate.toolkits import text_analytics
label_desc: Use image node_id 11 for calling the TextClassifier obj's underlying member method code with example usage: obj.evaluate(dataset, metric) and returns: m