parameters: (self, dataset, missing_value_action="auto")
return_statement: return super(BoostedTreesClassifier, self).classify(
              dataset, missing_value_action=missing_value_action
          )
docstring: Return a classification, for each example in the ``dataset``, using the
          trained boosted trees model. The output SFrame contains predictions
          as class labels (0 or 1) and probabilities associated with the the example.
  
          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. 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 i.e class labels and probabilities
              associated with each of the class labels.
  
          See Also
          ----------
          create, evaluate, predict
  
          Examples
          ----------
          >>> data =  turicreate.SFrame('https://static.turi.com/datasets/regression/houses.csv')
  
          >>> data['is_expensive'] = data['price'] > 30000
          >>> model = turicreate.boosted_trees_classifier.create(data,
          >>>                                                  target='is_expensive',
          >>>                                                  features=['bath', 'bedroom', 'size'])
  
          >>> classes = model.classify(data)
docstring_summary: Return a classification, for each example in the ``dataset``, using the
          trained boosted trees model. The output SFrame contains predictions
          as class labels (0 or 1) and probabilities associated with the the example.
docstring_tokens: Return a classification for each example in the dataset using the trained boosted trees model . The output SFrame contains predictions as class labels ( 0 or 1 ) and probabilities associated with the the example .
function: def classify(self, dataset, missing_value_action="auto"):
          """
          Return a classification, for each example in the ``dataset``, using the
          trained boosted trees model. The output SFrame contains predictions
          as class labels (0 or 1) and probabilities associated with the the example.
  
          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. 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 i.e class labels and probabilities
              associated with each of the class labels.
  
          See Also
          ----------
          create, evaluate, predict
  
          Examples
          ----------
          >>> data =  turicreate.SFrame('https://static.turi.com/datasets/regression/houses.csv')
  
          >>> data['is_expensive'] = data['price'] > 30000
          >>> model = turicreate.boosted_trees_classifier.create(data,
          >>>                                                  target='is_expensive',
          >>>                                                  features=['bath', 'bedroom', 'size'])
  
          >>> classes = model.classify(data)
  
          """
          return super(BoostedTreesClassifier, self).classify(
              dataset, missing_value_action=missing_value_action
          )
function_tokens: def classify ( self , dataset , missing_value_action = "auto" ) : return super ( BoostedTreesClassifier , self ) . classify ( dataset , missing_value_action = missing_value_action )