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: Plot generalized additive model component, which includes first-degree rules 
  and linear functions of unbinarized ordinal features but excludes higher-degree rules.
  
  Args:
      Xorig (DataFrame): Original unbinarized features
      fb: FeatureBinarizer object used to binarize features
      features (list, optional): Subset of features to be plotted
code: def visualize(self, Xorig, fb, features=None):
      """Plot generalized additive model component, which includes first-degree rules
      and linear functions of unbinarized ordinal features but excludes higher-degree rules.
  
      Args:
          Xorig (DataFrame): Original unbinarized features
          fb: FeatureBinarizer object used to binarize features
          features (list, optional): Subset of features to be plotted
      """
      return self._model.visualize(Xorig, fb, features)
  
file_path: AIX360/aix360/algorithms/rbm/GLRM.py
imports: from ..dise import DISExplainer
label_desc: Use image node_id 7 for calling the GLRMExplainer obj's underlying member method code with example usage: obj.visualize(Xorig, fb, features) and returns: self