class_docstr: TED is an explainability framework that leverages domain-relevant explanations in
  the training dataset to predict both labels and explanations for new instances [#]_.
  This is an implementation of the simplest instantiation of TED, called the Cartesian Product.
  
  References:
      .. [#] `Michael Hind, Dennis Wei, Murray Campbell, Noel C. F. Codella,
         Amit Dhurandhar, Aleksandra Mojsilovic, Karthikeyan Natesan Ramamurthy,
         Kush R. Varshney, "TED: Teaching AI to Explain its Decisions,"
         AAAI /ACM Conference on Artificial Intelligence, Ethics,
         and Society (AIES-19), 2019.
         <https://doi.org/10.1145/3306618.3314273>`_
class_docstr_tok: ["TED","is","an","explainability","framework","that","leverages","domain-relevant","explanations","in","the","training","dataset","to","predict","both","labels","and","explanations","for","new","instances","[","#","]","_",".","This","is","an","implementation","of","the","simplest","instantiation","of","TED",",","called","the","Cartesian","Product",".","References",":","..","[","#","]","`","Michael","Hind",",","Dennis","Wei",",","Murray","Campbell",",","Noel","C.","F.","Codella",",","Amit","Dhurandhar",",","Aleksandra","Mojsilovic",",","Karthikeyan","Natesan","Ramamurthy",",","Kush","R.","Varshney",",","``","TED",":","Teaching","AI","to","Explain","its","Decisions",",","''","AAAI","\/ACM","Conference","on","Artificial","Intelligence",",","Ethics",",","and","Society","(","AIES-19",")",",","2019",".","<","https",":","\/\/doi.org\/10.1145\/3306618.3314273",">","`","_"]
docstr: Use TED-enhanced classifier to provide an explanation (E) for passed instance
  
  Args:
      X (list of ints) : features
  Returns:
      int: predicted explanation [0..MaxE]
docstr_tok: ["Use","TED-enhanced","classifier","to","provide","an","explanation","(","E",")","for","passed","instance","Args",":","X","(","list","of","ints",")",":","features","Returns",":","int",":","predicted","explanation","[","0","..","MaxE","]"]
code: def explain(self, X):
      """Use TED-enhanced classifier to provide an explanation (E) for passed instance
  
      Args:
          X (list of ints) : features
      Returns:
          int: predicted explanation [0..MaxE]
      """
      Y, E = self.predict_explain(X)
      return E
  
file_path: AIX360/aix360/algorithms/ted/TED_Cartesian.py
imports: from aix360.algorithms.dise import DISExplainer
  from sklearn import metrics
label_desc: Use image node_id 5 for calling the TED_CartesianExplainer obj's underlying member method code with example usage: obj.explain(X) and returns: E