Body: <p>In the decision tree algorithm, why do we use a weighted average of child entropies when we calculate information gain? What is wrong about using the arithmetic mean of entropies?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-05-13T20:22:20.037Z
Comments.Text: Hi and welcome to AI SE! There are different decision tree algorithms. Which one are you referring to?
Comments.UserDisplayName: nbro
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-05-13T22:08:51.727Z
Comments.Text: @nbro Thank you! Well, in more general sense rather than particular one. It's part of an exercise to be honest...
Comments.UserDisplayName: Krushe
Tags: <decision-trees>
Title: Why do we use a weighted average of child entropies when we calculate information gain?
UserDisplayName: Krushe
ThreadText: # Why do we use a weighted average of child entropies when we calculate information gain?
  
  (Asked by: username_0 on 2020-05-13)
  
  In the decision tree algorithm, why do we use a weighted average of child entropies when we calculate information gain? What is wrong about using the arithmetic mean of entropies?
  
  **Tags:** decision-trees
  
  > **Comments:**
  > **username_1**: Hi and welcome to AI SE! There are different decision tree algorithms. Which one are you referring to?  
  > **username_0**: @username_1 Thank you! Well, in more general sense rather than particular one. It's part of an exercise to be honest...  
  
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  ## 0 answers
  
  