class_docstr: Generalized Linear Rule Model explainer. Provides access to the following
  directly interpretable supervised learning methods:
  
  * **Linear Rule Regression:** linear regression on rule-based features [#Icml2019]_.
  * **Logistic Rule Regression:** logistic regression on rule-based features [#Icml2019]_.
  
  References:
      .. [#Icml2019] `D. Wei, S. Dash, T. Gao, O. Günlük, "Generalized linear rule
         models." International Conference on Machine Learning (ICML), 2019.
         <http://proceedings.mlr.press/v97/wei19a/wei19a.pdf>`_
docstr: Fit model to training data.
  
  Args:
      X_train (DataFrame): Binarized features with MultiIndex column labels
      Y_train (array): Target variable
      Xstd (DataFrame, optional): Standardized numerical features
  Returns:
      GLRMExplainer: Self
code: def fit(self, X_train, Y_train, Xstd=None):
      """Fit model to training data.
  
      Args:
          X_train (DataFrame): Binarized features with MultiIndex column labels
          Y_train (array): Target variable
          Xstd (DataFrame, optional): Standardized numerical features
      Returns:
          GLRMExplainer: Self
      """
      self._model.fit(X_train, Y_train, Xstd)
  
file_path: AIX360/aix360/algorithms/rbm/GLRM.py
imports: from ..dise import DISExplainer
label_desc: Use image node_id 3 for calling the GLRMExplainer obj's underlying member method code with example usage: obj.fit(X_train, Y_train, Xstd) without return types