class_docstr: Classifier module to predict a discrete target variable as a function of
  several feature variables.
class_docstr_tok: ["Classifier","module","to","predict","a","discrete","target","variable","as","a","function","of","several","feature","variables","."]
docstr: Return predictions for ``dataset``, using the trained supervised_learning
  model. Predictions are generated as class labels (0 or
  1).
  
  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. This can be
      one of:
  
      - 'auto': Choose model dependent missing value action
      - '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 prediction and terminate with
        an error message.
  Returns
  -------
  out : SFrame
      An SFrame with model predictions.
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code: def classify(self, dataset, missing_value_action="auto"):
      """
      Return predictions for ``dataset``, using the trained supervised_learning
      model. Predictions are generated as class labels (0 or
      1).
  
      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. This can be
          one of:
  
          - 'auto': Choose model dependent missing value action
          - '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 prediction and terminate with
            an error message.
      Returns
      -------
      out : SFrame
          An SFrame with model predictions.
      """
      if missing_value_action == "auto":
          missing_value_action = select_default_missing_value_policy(
              self, "classify"
          )
  
      # Low latency path
      if isinstance(dataset, list):
          return self.__proxy__.fast_classify(
              dataset, missing_value_action
          )
      if isinstance(dataset, dict):
          return self.__proxy__.fast_classify(
              [dataset], missing_value_action
          )
  
      _raise_error_if_not_sframe(dataset, "dataset")
      return self.__proxy__.classify(dataset, missing_value_action)
  
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 2 for calling the Classifier obj's underlying member method code with example usage: obj.classify(dataset, missing_value_action) and returns: self, self, self