Answers.Body: <p>Are you thinking something like <a href="https://en.wikipedia.org/wiki/Decision_tree_learning#Information_gain" rel="nofollow noreferrer">Information Gain</a>?</p>
  
  <p>Information Gain basically uses the concept of information entropy to determine if splitting a variable is useful.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-10-21T13:55:33.667Z
Answers.LastActivityDate: 2019-10-21T13:55:33.667Z
Answers.UserDisplayName: user1209675
Body: <p>I want to know if there is a measure of how well two classes in Y are separable (linearly or not) based on their features in X. Easiest way of explaining this is to compare it to correlation coefficients, the higher the correlation the higher possiblity for successful regression based on given feature (at least in theory). </p>
  
  <p>Is there any measure that will tell me how well classes are separated based on input data features, before training a ML model? </p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-10-20T21:18:22.713Z
Comments.Text: You can use a different measure for the decision tree. Anyhow, it's an ML model.
Comments.UserDisplayName: OmG
Tags: <machine-learning><classification><statistical-ai>
Title: Is there any measure of separability of classes?
UserDisplayName: GKozinski
ThreadText: # Is there any measure of separability of classes?
  
  (Asked by: username_0 on 2019-10-20)
  
  I want to know if there is a measure of how well two classes in Y are separable (linearly or not) based on their features in X. Easiest way of explaining this is to compare it to correlation coefficients, the higher the correlation the higher possiblity for successful regression based on given feature (at least in theory).
  
  Is there any measure that will tell me how well classes are separated based on input data features, before training a ML model?
  
  **Tags:** machine-learning, classification, statistical-ai
  > **Comments:**
  > **username_1**: You can use a different measure for the decision tree. Anyhow, it's an ML model.  
  
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  ## 1 answers
  
  ### Answer by username_2 on 2019-10-21. Score: 2 | Accepted answer
  
  Are you thinking something like [Information Gain](https://en.wikipedia.org/wiki/Decision_tree_learning#Information_gain)?
  
  Information Gain basically uses the concept of information entropy to determine if splitting a variable is useful.
  
  
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