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: Fit model to training data.
  
  Args:
      X_train (DataFrame): Binarized features with MultiIndex column labels
      Y_train (array): Binary-valued target variable
  Returns:
      BRCGExplainer: Self
docstr_tok: ["Fit","model","to","training","data",".","Args",":","X_train","(","DataFrame",")",":","Binarized","features","with","MultiIndex","column","labels","Y_train","(","array",")",":","Binary-valued","target","variable","Returns",":","BRCGExplainer",":","Self"]
code: def fit(self, X_train, Y_train, *argv, **kwargs):
      """Fit model to training data.
  
      Args:
          X_train (DataFrame): Binarized features with MultiIndex column labels
          Y_train (array): Binary-valued target variable
      Returns:
          BRCGExplainer: Self
      """
      self._model.fit(X_train, Y_train, **kwargs)
  
imports: from ..dise import DISExplainer
label_desc: Use image node_id 3 for calling the BRCGExplainer obj's underlying member method code with example usage: obj.fit(X_train, Y_train) without return types