class_docstr: A class to test the TED_Cartesian explainer
class_docstr_tok: ["A","class","to","test","the","TED_Cartesian","explainer"]
docstr: This test evaluates the accuracy (in 3 ways) of the TED-enhanced classifier on the full test suite.
  It ensures the accuracy is above the correct values for this classifier.
  NOTE: accuracy will depend on the classifier used.
docstr_tok: ["This","test","evaluates","the","accuracy","(","in","3","ways",")","of","the","TED-enhanced","classifier","on","the","full","test","suite",".","It","ensures","the","accuracy","is","above","the","correct","values","for","this","classifier",".","NOTE",":","accuracy","will","depend","on","the","classifier","used","."]
code: def test_score(self):
      """
      This test evaluates the accuracy (in 3 ways) of the TED-enhanced classifier on the full test suite.
      It ensures the accuracy is above the correct values for this classifier.
      NOTE: accuracy will depend on the classifier used.
      """
      YE_accuracy, Y_accuracy, E_accuracy = self.ted.score(
          self.X_test, self.Y_test, self.E_test
      )  # evaluate the classifier
  
      # Expected Accuracy for this dataset and classifier
      # Accuracy of YE = 0.8510
      # Accuracy of Y = 0.8615
      # Accuracy of E = 0.8510
  
      self.assertGreaterEqual(YE_accuracy, 0.85)
      self.assertGreaterEqual(Y_accuracy, 0.85)
      self.assertGreaterEqual(E_accuracy, 0.85)
  
file_path: AIX360/tests/ted/test_TED_Cartesian.py
imports: import unittest
  import os
  import pandas
  from sklearn import svm
  from sklearn.model_selection import train_test_split
  from aix360.algorithms.ted import TED_CartesianExplainer
  from aix360.datasets.ted_dataset import TEDDataset
label_desc: Use image node_id 3 for calling the TestTED_Cartesian obj's underlying member method code with example usage: obj.test_score() without return types