Answers.Body: <p>If all you features are binary, then, you don't need to apply normalization on them. Since their values are on the same scale already.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2021-02-24T19:10:49.437Z
Answers.LastActivityDate: 2021-02-24T19:10:49.437Z
Answers.UserDisplayName: Abhishek Verma
Body: <p>By <a href="https://en.wikipedia.org/wiki/Feature_scaling" rel="nofollow noreferrer">scaling features</a>, we can prevent one feature from dominating the decisions of a model. For example, say heights (cm), and age (years) are two features in my data. Since range of heights is larger than of years, a trained model could weight importance of heights much more than years. This could result in a poor model in return.</p>
  <p>However, say that all of my features are binary, they take a value of either 0 or 1. In such a case, does feature scaling still have any benefits?</p>
  
Tags: <data-preprocessing><features><feature-engineering>
Title: Does feature scaling have any benefits if all features are on the same scale?
UserDisplayName: SpiderRico
ThreadText: # Does feature scaling have any benefits if all features are on the same scale?
  
  (Asked by: username_0 on 2021-02-24)
  
  By [scaling features](https://en.wikipedia.org/wiki/Feature_scaling), we can prevent one feature from dominating the decisions of a model. For example, say heights (cm), and age (years) are two features in my data. Since range of heights is larger than of years, a trained model could weight importance of heights much more than years. This could result in a poor model in return.
  
  However, say that all of my features are binary, they take a value of either 0 or 1. In such a case, does feature scaling still have any benefits?
  
  **Tags:** data-preprocessing, features, feature-engineering
  
  
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  ## 1 answers
  
  ### Answer by username_1 on 2021-02-24. Score: 2 | Accepted answer
  
  If all you features are binary, then, you don't need to apply normalization on them. Since their values are on the same scale already.
  
  
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