code: def __error_rate_rule_list(pos, neg, rule_list):
      """
      Calculate error rate of rules in pruning data set
  
      Parameters
      ----------
      pos : const double[::1, :]
          Positive instances of pruning data set
      neg : const double[::1, :]
          Negative instances of pruning data set
      rule_list: list
          Input rule list
  
      Returns
      -------
      double
          Error rate
      """
      return 1.0 - accuracy_tp_fp(
          len(pos),
          len(neg),
          np.count_nonzero(_rule_list_predict(pos, rule_list)),
          np.count_nonzero(_rule_list_predict(neg, rule_list)),
      )
  
file_path: AIX360/aix360/algorithms/rule_induction/ripper/pruning.py
imports: import numba
  import numpy
  from aix360.algorithms.rule_induction.ripper.binding import _rule_predict, _rule_list_predict
  from aix360.algorithms.rule_induction.ripper.mdl import _mdl
label_desc: Use image node_id 4 for calling a global function with example usage: __error_rate_rule_list(pos, neg, rule_list) and returns: unknown