code: def CEN_misclassification_calc(
      table, TOP, P, i, j, subject_class, modified=False
  ):
      """
      Calculate Misclassification probability.
  
      :param table: input confusion matrix
      :type table: dict
      :param TOP: number of positives in predict vector
      :type TOP: int
      :param P: number of actual positives
      :type P: int
      :param i: table row index (class name)
      :type i: any valid type
      :param j: table col index (class name)
      :type j: any valid type
      :param subject_class: subject to class (class name)
      :type subject_class: any valid type
      :param modified: modified mode flag
      :type modified: bool
      :return: misclassification probability as float
      """
      try:
          result = TOP + P
          if modified:
              result -= table[subject_class][subject_class]
          result = table[i][j] / result
          return result
      except (ZeroDivisionError, TypeError):
          return "None"
  
imports: from __future__ import division
  import math
  from .pycm_util import normal_quantile
  from .pycm_interpret import *
  from .pycm_param import CLASS_PARAMS
label_desc: Use image node_id 24 for calling a global function with example usage: CEN_misclassification_calc(table, TOP, P, i, j, subject_class, modified) and returns: result, str