code: def conditional_entropy_calc(classes, table, P, POP):
      
      try:
          result = 0
          for i in classes:
              temp = 0
              for index, j in enumerate(classes):
                  p_prime = 0
                  if P[i] != 0:
                      p_prime = table[i][j] / P[i]
                  if p_prime != 0:
                      temp += p_prime * math.log(p_prime, 2)
              result += temp * (P[i] / POP[i])
          return -result
      except Exception:
          return "None"
docs: Calculate conditional entropy.
  
      :param classes: confusion matrix classes
      :type classes : list
      :param table: confusion matrix table
      :type table : dict
      :param P: condition positive
      :type P : dict
      :param POP: population
      :type POP : dict
      :return: conditional entropy as float