parameters: (
          self, dataset, output_type="probability", k=3, missing_value_action="auto"
      )
return_statement: return self.__proxy__.predict_topk(
              dataset, missing_value_action, output_type, k
          )
docstring: Return top-k predictions for the ``dataset``, using the trained model.
          Predictions are returned as an SFrame with three columns: `id`,
          `class`, and `probability`, `margin`,  or `rank`, depending on the ``output_type``
          parameter. Input dataset size must be the same as for training of the model.
  
          Parameters
          ----------
          dataset : SFrame
              A dataset that has the same columns that were used during training.
              If the target column exists in ``dataset`` it will be ignored
              while making predictions.
  
          output_type : {'probability', 'rank', 'margin'}, optional
              Choose the return type of the prediction:
  
              - `probability`: Probability associated with each label in the prediction.
              - `rank`       : Rank associated with each label in the prediction.
              - `margin`     : Margin associated with each label in the prediction.
  
          k : int, optional
              Number of classes to return for each input example.
  
          missing_value_action : str, optional
              Action to perform when missing values are encountered. Can be
              one of:
  
              - 'auto': By default the model will treat missing value as is.
              - '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 evaluation and terminate with
                an error message.
  
          Returns
          -------
          out : SFrame
              An SFrame with model predictions.
  
          See Also
          --------
          predict, classify, evaluate
  
          Examples
          --------
          >>> pred = m.predict_topk(validation_data, k=3)
          >>> pred
          +--------+-------+-------------------+
          | id     | class |   probability     |
          +--------+-------+-------------------+
          |   0    |   4   |   0.995623886585  |
          |   0    |   9   |  0.0038311756216  |
          |   0    |   7   | 0.000301006948575 |
          |   1    |   1   |   0.928708016872  |
          |   1    |   3   |  0.0440889261663  |
          |   1    |   2   |  0.0176190119237  |
          |   2    |   3   |   0.996967732906  |
          |   2    |   2   |  0.00151345680933 |
          |   2    |   7   | 0.000637513934635 |
          |   3    |   1   |   0.998070061207  |
          |  ...   |  ...  |        ...        |
          +--------+-------+-------------------+
          [35688 rows x 3 columns]
docstring_summary: Return top-k predictions for the ``dataset``, using the trained model.
          Predictions are returned as an SFrame with three columns: `id`,
          `class`, and `probability`, `margin`,  or `rank`, depending on the ``output_type``
          parameter. Input dataset size must be the same as for training of the model.
docstring_tokens: Return top - k predictions for the dataset using the trained model . Predictions are returned as an SFrame with three columns : id class and probability margin or rank depending on the output_type parameter . Input dataset size must be the same as for training of the model .
function: def predict_topk(
          self, dataset, output_type="probability", k=3, missing_value_action="auto"
      ):
          """
          Return top-k predictions for the ``dataset``, using the trained model.
          Predictions are returned as an SFrame with three columns: `id`,
          `class`, and `probability`, `margin`,  or `rank`, depending on the ``output_type``
          parameter. Input dataset size must be the same as for training of the model.
  
          Parameters
          ----------
          dataset : SFrame
              A dataset that has the same columns that were used during training.
              If the target column exists in ``dataset`` it will be ignored
              while making predictions.
  
          output_type : {'probability', 'rank', 'margin'}, optional
              Choose the return type of the prediction:
  
              - `probability`: Probability associated with each label in the prediction.
              - `rank`       : Rank associated with each label in the prediction.
              - `margin`     : Margin associated with each label in the prediction.
  
          k : int, optional
              Number of classes to return for each input example.
  
          missing_value_action : str, optional
              Action to perform when missing values are encountered. Can be
              one of:
  
              - 'auto': By default the model will treat missing value as is.
              - '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 evaluation and terminate with
                an error message.
  
          Returns
          -------
          out : SFrame
              An SFrame with model predictions.
  
          See Also
          --------
          predict, classify, evaluate
  
          Examples
          --------
          >>> pred = m.predict_topk(validation_data, k=3)
          >>> pred
          +--------+-------+-------------------+
          | id     | class |   probability     |
          +--------+-------+-------------------+
          |   0    |   4   |   0.995623886585  |
          |   0    |   9   |  0.0038311756216  |
          |   0    |   7   | 0.000301006948575 |
          |   1    |   1   |   0.928708016872  |
          |   1    |   3   |  0.0440889261663  |
          |   1    |   2   |  0.0176190119237  |
          |   2    |   3   |   0.996967732906  |
          |   2    |   2   |  0.00151345680933 |
          |   2    |   7   | 0.000637513934635 |
          |   3    |   1   |   0.998070061207  |
          |  ...   |  ...  |        ...        |
          +--------+-------+-------------------+
          [35688 rows x 3 columns]
          """
          _check_categorical_option_type(
              "output_type", output_type, ["rank", "margin", "probability"]
          )
          if missing_value_action == "auto":
              missing_value_action = _sl.select_default_missing_value_policy(
                  self, "predict"
              )
  
          # Low latency path
          if isinstance(dataset, list):
              return self.__proxy__.fast_predict_topk(
                  dataset, missing_value_action, output_type, k
              )
          if isinstance(dataset, dict):
              return self.__proxy__.fast_predict_topk(
                  [dataset], missing_value_action, output_type, k
              )
          # Fast path
          _raise_error_if_not_sframe(dataset, "dataset")
          return self.__proxy__.predict_topk(
              dataset, missing_value_action, output_type, k
          )
function_tokens: def predict_topk ( self , dataset , output_type = "probability" , k = 3 , missing_value_action = "auto" ) : _check_categorical_option_type ( "output_type" , output_type , [ "rank" , "margin" , "probability" ] ) if missing_value_action == "auto" : missing_value_action = _sl . select_default_missing_value_policy ( self , "predict" ) # Low latency path if isinstance ( dataset , list ) : return self . __proxy__ . fast_predict_topk ( dataset , missing_value_action , output_type , k ) if isinstance ( dataset , dict ) : return self . __proxy__ . fast_predict_topk ( [ dataset ] , missing_value_action , output_type , k ) # Fast path _raise_error_if_not_sframe ( dataset , "dataset" ) return self . __proxy__ . predict_topk ( dataset , missing_value_action , output_type , k )