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: Return DataFrame holding model features and their coefficients.
  
  Args:
      maxCoeffs (int, optional): Maximum number of rules/numerical features to show
      highDegOnly (bool, optional): Only show higher-degree rules
      prec (int, optional): Number of decimal places to show for floating-value thresholds
  Returns:
      DataFrame: dfExpl -- Rules/numerical features and their coefficients
code: def explain(self, maxCoeffs=None, highDegOnly=False, prec=2):
      """Return DataFrame holding model features and their coefficients.
  
      Args:
          maxCoeffs (int, optional): Maximum number of rules/numerical features to show
          highDegOnly (bool, optional): Only show higher-degree rules
          prec (int, optional): Number of decimal places to show for floating-value thresholds
      Returns:
          DataFrame: dfExpl -- Rules/numerical features and their coefficients
      """
      return self._model.explain(maxCoeffs, highDegOnly, prec)
  
file_path: AIX360/aix360/algorithms/rbm/GLRM.py
imports: from ..dise import DISExplainer
label_desc: Use image node_id 6 for calling the GLRMExplainer obj's underlying member method code with example usage: obj.explain(maxCoeffs, highDegOnly, prec) and returns: self