class_docstr: Base class for the tree ensemble builder class.  This should be instantiated
  either through the :py:class:`TreeEnsembleRegressor` or
  :py:class:`TreeEnsembleClassifier` classes.
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docstr: 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.
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code: 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
          )
      )
  
file_path: coremltools/coremltools/models/tree_ensemble.py
imports: import collections
  from ..None import SPECIFICATION_VERSION
  from ..proto import Model_pb2
  from ..proto import TreeEnsemble_pb2
  from ._interface_management import set_classifier_interface_params, set_regressor_interface_params
label_desc: Use image node_id 3 for calling the TreeEnsembleBase obj's underlying member method code with example usage: obj.set_post_evaluation_transform(value) without return types