parameters: (self, value)
docstring: 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.
docstring_summary: r"""
          Set the post processing transform applied after the prediction value
          from the tree ensemble.
docstring_tokens: r Set the post processing transform applied after the prediction value from the tree ensemble .
function: 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
          )
function_tokens: def set_post_evaluation_transform ( self , value ) : self . tree_spec . postEvaluationTransform = _TreeEnsemble_pb2 . TreeEnsemblePostEvaluationTransform . Value ( value )