class_docstr: Grid search reduction for fair classification or regression.
  
  Grid search is an in-processing technique that can be used for fair
  classification or fair regression. For classification it reduces fair
  classification to a sequence of cost-sensitive classification problems,
  returning the deterministic classifier with the lowest empirical error
  subject to fair classification constraints [#agarwal18]_ among the
  candidates searched. For regression it uses the same priniciple to return a
  deterministic regressor with the lowest empirical error subject to the
  constraint of bounded group loss [#agarwal19]_.
  
  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>`_
      .. [#agarwal19] `A. Agarwal, M. Dudik, and Z. Wu, "Fair Regression:
         Quantitative Definitions and Reduction-based Algorithms,"
         International Conference on Machine Learning, 2019.
         <https://arxiv.org/abs/1905.12843>`_
docstr: Predict output for the given samples.
  
  Args:
      X (pandas.DataFrame): Test samples.
  
  Returns:
      numpy.ndarray: Predicted output per sample.
code: def predict(self, X):
      """Predict output for the given samples.
  
      Args:
          X (pandas.DataFrame): Test samples.
  
      Returns:
          numpy.ndarray: Predicted output per sample.
      """
      if self.drop_prot_attr:
          X = X.drop(self.prot_attr, axis=1)
  
      return self.model_.predict(X)
  
file_path: AIF360/aif360/sklearn/inprocessing/grid_search_reduction.py
imports: from sklearn.base import BaseEstimator, ClassifierMixin, clone
  from sklearn.preprocessing import LabelEncoder
label_desc: Use image node_id 3 for calling the GridSearchReduction obj's underlying member method code with example usage: obj.predict(X) and returns: self