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: 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 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.
  
  output_type : str, optional
      output type that maybe needed by some of the toolkits
  
  options : dict
      additional options to be passed in to prediction
  
  kwargs : dict
      additional options to be passed into prediction
  
  Returns
  -------
  out : SArray
      An SArray with model predictions.
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code: def predict(
      self,
      dataset,
      missing_value_action="auto",
      output_type="",
      options={},
      **kwargs,
  ):
      """
      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 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.
  
      output_type : str, optional
          output type that maybe needed by some of the toolkits
  
      options : dict
          additional options to be passed in to prediction
  
      kwargs : dict
          additional options to be passed into prediction
  
      Returns
      -------
      out : SArray
          An SArray with model predictions.
      """
      if missing_value_action == "auto":
          missing_value_action = select_default_missing_value_policy(
              self, "predict"
          )
  
      # Low latency path
      if isinstance(dataset, list):
          return self.__proxy__.fast_predict(
              dataset, missing_value_action, output_type
          )
      if isinstance(dataset, dict):
          return self.__proxy__.fast_predict(
              [dataset], missing_value_action, output_type
          )
  
      # Batch predictions path
      else:
          _raise_error_if_not_sframe(dataset, "dataset")
  
          return self.__proxy__.predict(
              dataset, missing_value_action, output_type
          )
  
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 5 for calling the SupervisedLearningModel obj's underlying member method code with example usage: obj.predict(dataset, missing_value_action, output_type, options) and returns: self, self, self