code: def statistic_recommend(classes, imbalance):
      """
      Return recommend parameters which are more suitable due to the input dataset characteristics.
  
      :param classes: confusion matrix classes
      :type classes: list
      :param imbalance: imbalance flag (True: imbalance, False: balance)
      :type imbalance: bool
      :return: recommendation metrics as list
      """
      if imbalance:
          return IMBALANCED_RECOMMEND
      if binary_check(classes):
          return BINARY_RECOMMEND
      return MULTICLASS_RECOMMEND
  
imports: from __future__ import division
  import sys
  import math
  import numpy
  import re
  from .pycm_param import *
  from .pycm_error import pycmMatrixError
  from warnings import warn
  from functools import wraps
label_desc: Use image node_id 21 for calling a global function with example usage: statistic_recommend(classes, imbalance) and returns: MULTICLASS_RECOMMEND, IMBALANCED_RECOMMEND, BINARY_RECOMMEND