Answers.Body: <p>You should check the distribution of each feature and scale them accordingly, but in any case you should aim to roughly the same interval of values for every feature.
  For example, if f1 has the standard distribution and f2 is close to the uniform one, then you can scale f1 to N(0,1) and f2 to U(-1,1).
  In other words, try to have maximum, minimum and mean values for all features as close as possible, while keeping their original distributions.</p>
  
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Answers.CreationDate: 2020-06-19T10:23:24.577Z
Answers.LastActivityDate: 2020-06-19T10:23:24.577Z
Answers.UserDisplayName: oleg.mosalov
Answers.Body: <p>You should normalize every column individually. It will work just fine. Sum up the column and divide every element of that column by sum of that column. But as your feature 2,3,4 are of very small scale you should consider some transformation like log transformation as you might encounter numerical underflow.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2020-06-19T13:36:49.770Z
Answers.Text: What is log transformation for feature 2 3 4 ? Could you explain a little bit clearer ?
  Thank you very much
Answers.UserDisplayName: Tuong Nguyen Minh
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2020-06-19T13:39:20.723Z
Answers.Text: Instead of x,  use log(1+x) or something like it. It will prevent numerical underflow
Answers.UserDisplayName: SrJ
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Answers.CreationDate: 2020-06-19T12:20:45.990Z
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Answers.UserDisplayName: SrJ
Body: <p>I was running into a situation in which my input feature experience a very large variation in term of magnitude.</p>
  <p>Particularly, consider feature 1 belong to group 1 and feature 2 3 4 belong to group 2,</p>
  <p>Like this picture below</p>
  <p><a href="https://i.sstatic.net/4FHOK.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/4FHOK.png" alt="enter image description here" /></a></p>
  <p>I was really worried that in this case <strong>feature 1 might dominate feature 2,3,4 (group 2)</strong> because its corresponding value is so large (I was trying to train this data set on a neural network).</p>
  <p>In this situation, what would be the appropriate scaling strategy ?
  Update: I know for sure that the value of feature 1 is an integer that is uniform on the interval [22,42]
  But for feature 2 ,3 ,4 I do not have any insight</p>
  <p>Thank you for your enthusiast !</p>
  
Tags: <neural-networks><features>
Title: Feature scaling strategy for many feature with very large variation between them?
UserDisplayName: Tuong Nguyen Minh
ThreadText: # Feature scaling strategy for many feature with very large variation between them?
  
  (Asked by: username_0 on 2020-06-19)
  
  I was running into a situation in which my input feature experience a very large variation in term of magnitude.
  
  Particularly, consider feature 1 belong to group 1 and feature 2 3 4 belong to group 2,
  
  Like this picture below
  
  [![enter image description here](https://i.sstatic.net/4FHOK.png)](https://i.sstatic.net/4FHOK.png)
  
  I was really worried that in this case **feature 1 might dominate feature 2,3,4 (group 2)** because its corresponding value is so large (I was trying to train this data set on a neural network).
  
  In this situation, what would be the appropriate scaling strategy ?
  Update: I know for sure that the value of feature 1 is an integer that is uniform on the interval [22,42]
  But for feature 2 ,3 ,4 I do not have any insight
  
  Thank you for your enthusiast !
  
  **Tags:** neural-networks, features
  
  
  ---
  
  ## 2 answers
  
  ### Answer by username_1 on 2020-06-19. Score: 1
  
  You should check the distribution of each feature and scale them accordingly, but in any case you should aim to roughly the same interval of values for every feature.
  For example, if f1 has the standard distribution and f2 is close to the uniform one, then you can scale f1 to N(0,1) and f2 to U(-1,1).
  In other words, try to have maximum, minimum and mean values for all features as close as possible, while keeping their original distributions.
  
  
  ---
  
  ### Answer by username_2 on 2020-06-19. Score: 1
  
  You should normalize every column individually. It will work just fine. Sum up the column and divide every element of that column by sum of that column. But as your feature 2,3,4 are of very small scale you should consider some transformation like log transformation as you might encounter numerical underflow.
  
  > **Comments:**
  > **username_0**: What is log transformation for feature 2 3 4 ? Could you explain a little bit clearer ? Thank you very much  
  > **username_2**: Instead of x,  use log(1+x) or something like it. It will prevent numerical underflow  
  
  ---
  
  