class_docstr: Exponentiated gradient reduction for fair classification.
  
  Exponentiated gradient reduction is an in-processing technique that reduces
  fair classification to a sequence of cost-sensitive classification problems,
  returning a randomized classifier with the lowest empirical error subject to
  fair classification constraints [#agarwal18]_.
  
  References:
      .. [#agarwal18] `A. Agarwal, A. Beygelzimer, M. Dudik, J. Langford, and
         H. Wallach, "A Reductions Approach to Fair Classification,"
         International Conference on Machine Learning, 2018.
         <https://arxiv.org/abs/1803.02453>`_
docstr: Probability estimates.
  
  The returned estimates for all classes are ordered by the label of
  classes.
  
  Args:
      X (pandas.DataFrame): Test samples.
  
  Returns:
      numpy.ndarray: Returns the probability of the sample for each class
      in the model, where classes are ordered as they are in
      ``self.classes_``.
code: def predict_proba(self, X):
      """Probability estimates.
  
      The returned estimates for all classes are ordered by the label of
      classes.
  
      Args:
          X (pandas.DataFrame): Test samples.
  
      Returns:
          numpy.ndarray: Returns the probability of the sample for each class
          in the model, where classes are ordered as they are in
          ``self.classes_``.
      """
      if self.drop_prot_attr:
          X = X.drop(self.prot_attr, axis=1)
  
      return self.model_._pmf_predict(X)
  
file_path: AIF360/aif360/sklearn/inprocessing/exponentiated_gradient_reduction.py
imports: from sklearn.base import BaseEstimator, ClassifierMixin, clone
  from sklearn.preprocessing import LabelEncoder
label_desc: Use image node_id 4 for calling the ExponentiatedGradientReduction obj's underlying member method code with example usage: obj.predict_proba(X) and returns: self