class_docstr: Supervised learning module to predict a target variable as a function of
  several feature variables.
class_docstr_tok: ["Supervised","learning","module","to","predict","a","target","variable","as","a","function","of","several","feature","variables","."]
docstr: Evaluate the model by making predictions of target values and comparing
  these to actual values.
  
  Parameters
  ----------
  dataset : SFrame
      Dataset in the same format used for training. The columns names and
      types of the dataset must be the same as that used in training.
  
  metric : str, list[str]
      Evaluation metric(s) to be computed.
  
  missing_value_action: str, optional
      Action to perform when missing values are encountered. This can be
      one of:
  
      - 'auto': Choose a model dependent missing value policy.
      - '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.
      - 'none': Treat missing value as is. Model must be able to handle missing value.
      - 'error' : Do not proceed with prediction and terminate with
                  an error message.
  
  options : dict
      additional options to be passed in to prediction
  
  kwargs : dict
      additional options to be passed into prediction
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code: def evaluate(
      self,
      dataset,
      metric="auto",
      missing_value_action="auto",
      with_predictions=False,
      options={},
      **kwargs,
  ):
      """
      Evaluate the model by making predictions of target values and comparing
      these to actual values.
  
      Parameters
      ----------
      dataset : SFrame
          Dataset in the same format used for training. The columns names and
          types of the dataset must be the same as that used in training.
  
      metric : str, list[str]
          Evaluation metric(s) to be computed.
  
      missing_value_action: str, optional
          Action to perform when missing values are encountered. This can be
          one of:
  
          - 'auto': Choose a model dependent missing value policy.
          - '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.
          - 'none': Treat missing value as is. Model must be able to handle missing value.
          - 'error' : Do not proceed with prediction and terminate with
                      an error message.
  
      options : dict
          additional options to be passed in to prediction
  
      kwargs : dict
          additional options to be passed into prediction
      """
      if missing_value_action == "auto":
          missing_value_action = select_default_missing_value_policy(
              self, "evaluate"
          )
  
      _raise_error_if_not_sframe(dataset, "dataset")
      results = self.__proxy__.evaluate(
          dataset,
          missing_value_action,
          metric,
          with_predictions=with_predictions,
      )
      return results
  
imports: from __future__ import print_function
  from __future__ import division
  from __future__ import absolute_import
  import turicreate
  from turicreate.toolkits._model import Model
  from turicreate.toolkits._internal_utils import _raise_error_if_not_sframe
  from turicreate.toolkits._internal_utils import _validate_data
  from turicreate.toolkits._main import ToolkitError
  from turicreate._cython.cy_server import QuietProgress
label_desc: Use image node_id 6 for calling the SupervisedLearningModel obj's underlying member method code with example usage: obj.evaluate(dataset, metric, missing_value_action, with_predictions, options) and returns: results