class_docstr: Boolean Rule Column Generation explainer. Provides access to 
  :class:`aix360.algorithms.rbm.boolean_rule_cg.BooleanRuleCG`, which
  implements a directly interpretable supervised learning method
  for binary classification that learns a Boolean rule in disjunctive
  normal form (DNF) or conjunctive normal form (CNF) using column generation (CG).
  AIX360 implements a heuristic beam search version of BRCG that is less 
  computationally intensive than the published integer programming version [#NeurIPS2018]_.
  
  References:
      .. [#NeurIPS2018] `S. Dash, O. Günlük, D. Wei, "Boolean decision rules via
         column generation." Neural Information Processing Systems (NeurIPS), 2018.
         <https://papers.nips.cc/paper/7716-boolean-decision-rules-via-column-generation.pdf>`_
docstr: Predict class labels.
  
  Args:
      X (DataFrame): Binarized features with MultiIndex column labels
  Returns:
      array: y -- Predicted labels
docstr_tok: ["Predict","class","labels",".","Args",":","X","(","DataFrame",")",":","Binarized","features","with","MultiIndex","column","labels","Returns",":","array",":","y","--","Predicted","labels"]
code: def predict(self, X, *argv, **kwargs):
      """Predict class labels.
  
      Args:
          X (DataFrame): Binarized features with MultiIndex column labels
      Returns:
          array: y -- Predicted labels
      """
      return self._model.predict(X, **kwargs)
  
imports: from ..dise import DISExplainer
label_desc: Use image node_id 4 for calling the BRCGExplainer obj's underlying member method code with example usage: obj.predict(X) and returns: self