class_docstr: Information Gain split criterion.
  
  A measure of how often a randomly chosen element from the set would be
  incorrectly labeled if it was randomly labeled according to the
  distribution of labels in the subset.
  
  References
  ----------
  [Wikipedia entry](https://en.wikipedia.org/wiki/Decision_tree_learning#Information_gain)
class_docstr_tok: ["Information","Gain","split","criterion",".","A","measure","of","how","often","a","randomly","chosen","element","from","the","set","would","be","incorrectly","labeled","if","it","was","randomly","labeled","according","to","the","distribution","of","labels","in","the","subset",".","References","--","--","--","--","--","[","Wikipedia","entry","]","(","https",":","\/\/en.wikipedia.org\/wiki\/Decision_tree_learning","#","Information_gain",")"]
code: def _compute_entropy_list(self, dists):
      total_weight = 0.0
      dist_weights = [0.0] * len(dists)
      for i in range(len(dists)):
          dist_weights[i] = sum(dists[i].values())
          total_weight += dist_weights[i]
      entropy = 0.0
      for i in range(len(dists)):
          entropy += dist_weights[i] * self.compute_entropy(dists[i])
      return entropy / total_weight
  
file_path: river/river/tree/split_criterion/info_gain_split_criterion.py
imports: from __future__ import annotations
  import math
  from .base import SplitCriterion
label_desc: Use image node_id 6 for calling the InfoGainSplitCriterion obj's underlying member method code with example usage: obj._compute_entropy_list(dists) and returns: unknown