For each example in the dataset, extract the leaf indices of
        each tree as features.

        For multiclass classification, each leaf index contains #num_class
        numbers.

        The returned feature vectors can be used as input to train another
        supervised learning model such as a
        :py:class:`~turicreate.logistic_classifier.LogisticClassifier`,
        an :py:class:`~turicreate.svm_classifier.SVMClassifier`, or a

        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. This can be
            one of:

            - 'auto': Choose a model dependent missing value policy.
            - '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.
            - 'none': Treat missing value as is. Model must be able to handle
                      missing value.
            - 'error' : Do not proceed with prediction and terminate with
                        an error message.

        Returns
        -------
        out : SArray
            An SArray of dtype array.array containing extracted features.

        Examples
        --------
        >>> data =  turicreate.SFrame(
            'https://static.turi.com/datasets/regression/houses.csv')

        >>> # Regression Tree Models
        >>> data['regression_tree_features'] = model.extract_features(data)

        >>> # Classification Tree Models
        >>> data['classification_tree_features'] = model.extract_features(data)

def extract_features(self, dataset, missing_value_action='auto'):
        """
        For each example in the dataset, extract the leaf indices of
        each tree as features.

        For multiclass classification, each leaf index contains #num_class
        numbers.

        The returned feature vectors can be used as input to train another
        supervised learning model such as a
        :py:class:`~turicreate.logistic_classifier.LogisticClassifier`,
        an :py:class:`~turicreate.svm_classifier.SVMClassifier`, or a

        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. This can be
            one of:

            - 'auto': Choose a model dependent missing value policy.
            - '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.
            - 'none': Treat missing value as is. Model must be able to handle
                      missing value.
            - 'error' : Do not proceed with prediction and terminate with
                        an error message.

        Returns
        -------
        out : SArray
            An SArray of dtype array.array containing extracted features.

        Examples
        --------
        >>> data =  turicreate.SFrame(
            'https://static.turi.com/datasets/regression/houses.csv')

        >>> # Regression Tree Models
        >>> data['regression_tree_features'] = model.extract_features(data)

        >>> # Classification Tree Models
        >>> data['classification_tree_features'] = model.extract_features(data)
        """
        _raise_error_if_not_sframe(dataset, "dataset")
        if missing_value_action == 'auto':
            missing_value_action = select_default_missing_value_policy(self,
                    'extract_features')

        return self.__proxy__.extract_features(dataset, missing_value_action)