code: def predict(self, dataset, output_type='class'):
          
          m = self.__proxy__['classifier']
          target = self.__proxy__['target']
          f = _BOW_FEATURE_EXTRACTOR
          return m.predict(f(dataset, target), output_type=output_type)
docs: Return predictions for ``dataset``, using the trained model.
  
          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.
  
          output_type : {'class', 'probability_vector'}, optional
              Form of the predictions which are one of:
  
              - 'probability_vector': Prediction probability associated with each
                class as a vector. The probability of the first class (sorted
                alphanumerically by name of the class in the training set) is in
                position 0 of the vector, the second in position 1 and so on.
              - 'class': Class prediction. For multi-class classification, this
                returns the class with maximum probability.
  
          Returns
          -------
          out : SArray
              An SArray with model predictions.
  
          See Also
          ----------
          create, evaluate, classify
  
  
          Examples
          --------
          >>> import turicreate as tc
          >>> dataset = tc.SFrame({'rating': [1, 5], 'text': ['hate it', 'love it']})
          >>> m = tc.text_classifier.create(dataset, 'rating', features=['text'])
          >>> m.predict(dataset)