r"""
        Set the post processing transform applied after the prediction value
        from the tree ensemble.

        Parameters
        ----------

        value: str

            A value denoting the transform applied.  Possible values are:

            - "NoTransform" (default).  Do not apply a transform.

            - "Classification_SoftMax".

              Apply a softmax function to the outcome to produce normalized,
              non-negative scores that sum to 1.  The transformation applied to
              dimension `i` is equivalent to:

                .. math::

                    \frac{e^{x_i}}{\sum_j e^{x_j}}

              Note: This is the output transformation applied by the XGBoost package
              with multiclass classification.

            - "Regression_Logistic".

              Applies a logistic transform the predicted value, specifically:

                .. math::

                    (1 + e^{-v})^{-1}

              This is the transformation used in binary classification.

def set_post_evaluation_transform(self, value):
        r"""
        Set the post processing transform applied after the prediction value
        from the tree ensemble.

        Parameters
        ----------

        value: str

            A value denoting the transform applied.  Possible values are:

            - "NoTransform" (default).  Do not apply a transform.

            - "Classification_SoftMax".

              Apply a softmax function to the outcome to produce normalized,
              non-negative scores that sum to 1.  The transformation applied to
              dimension `i` is equivalent to:

                .. math::

                    \frac{e^{x_i}}{\sum_j e^{x_j}}

              Note: This is the output transformation applied by the XGBoost package
              with multiclass classification.

            - "Regression_Logistic".

              Applies a logistic transform the predicted value, specifically:

                .. math::

                    (1 + e^{-v})^{-1}

              This is the transformation used in binary classification.


        """
        self.tree_spec.postEvaluationTransform = \
                _TreeEnsemble_pb2.TreeEnsemblePostEvaluationTransform.Value(value)