def classify(self, dataset, missing_value_action='auto'):
        """
        Return a classification, for each example in the ``dataset``, using the
        trained random forest 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.random_forest_classifier.create(data,
        >>>                                                  target='is_expensive',
        >>>                                                  features=['bath', 'bedroom', 'size'])
        >>> classes = model.classify(data)
        """
        return super(RandomForestClassifier, self).classify(dataset,
                                                            missing_value_action=missing_value_action)
def classify ( self , dataset , missing_value_action = 'auto' ) : return super ( RandomForestClassifier , self ) . classify ( dataset , missing_value_action = missing_value_action )
Return a classification, for each example in the ``dataset``, using the
        trained random forest 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.random_forest_classifier.create(data,
        >>>                                                  target='is_expensive',
        >>>                                                  features=['bath', 'bedroom', 'size'])
        >>> classes = model.classify(data)