code: def classify(self, dataset):
          
          m = self.__proxy__['classifier']
          target = self.__proxy__['target']
          f = _BOW_FEATURE_EXTRACTOR
          return m.classify(f(dataset, target))
docs: Return a classification, for each example in the ``dataset``, using the
          trained model. The output SFrame contains predictions as both class
          labels as well as probabilities that the predicted value is the
          associated label.
  
          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.
  
          Returns
          -------
          out : SFrame
              An SFrame with model predictions i.e class labels and probabilities.
  
          See Also
          ----------
          create, evaluate, predict
  
          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'])
          >>> output = m.classify(dataset)