In this article, we shall discuss one of the ubiquitous steps in the machine learning pipeline — Feature Scaling. This article's origin lies in one of the coffee discussions in my office on what all models actually are affected by feature scaling and then what is the best way to do it — to normalize or to standardize or something else?

In this article, in addition to the above, we would also cover a gentle introduction to feature scaling, the various feature scaling techniques, how it might lead to data leakage, when to perform feature scaling, and when NOT to perform feature scaling. …


Raghav Vashisht

Master in Data Science and Business Analytics, ESSEC Business School- CentraleSupélec

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