code: def classify(self, dataset, output_frequency='per_row'):
          
          _tkutl._check_categorical_option_type(
              'output_frequency', output_frequency, ['per_window', 'per_row'])
          id_target_map = self._id_target_map
          preds = self.predict(
              dataset, output_type='probability_vector', output_frequency=output_frequency)
  
          if output_frequency == 'per_row':
              return _SFrame({
                  'class': preds.apply(lambda p: id_target_map[_np.argmax(p)]),
                  'probability': preds.apply(_np.max)
              })
          elif output_frequency == 'per_window':
              preds['class'] = preds['probability_vector'].apply(
                  lambda p: id_target_map[_np.argmax(p)])
              preds['probability'] = preds['probability_vector'].apply(_np.max)
              preds = preds.remove_column('probability_vector')
              return preds
docs: Return a classification, for each ``prediction_window`` examples in the
          ``dataset``, using the trained activity classification 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 and session id used for model training, but
              does not require a target column. Additional columns are ignored.
  
          output_frequency : {'per_row', 'per_window'}, optional
              The frequency of the predictions which is one of:
  
              - 'per_row': Each prediction is returned ``prediction_window`` times.
              - 'per_window': Return a single prediction for each 
                ``prediction_window`` rows in ``dataset`` per ``session_id``.
  
          Returns
          -------
          out : SFrame
              An SFrame with model predictions i.e class labels and probabilities.
  
          See Also
          ----------
          create, evaluate, predict
  
          Examples
          ----------
          >>> classes = model.classify(data)