<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>

CC BY-SA 4.0
2021-02-24T19:10:49.437Z
2021-02-24T19:10:49.437Z
Abhishek Verma
<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>

<data-preprocessing><features><feature-engineering>
Does feature scaling have any benefits if all features are on the same scale?
SpiderRico
# 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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