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Machine Learning: Understand Centering and Scaling purposes
Data Analysis   Latest   Machine Learning

Machine Learning: Understand Centering and Scaling purposes

Last Updated on November 6, 2023 by Editorial Team

Author(s): Flo

Originally published on Towards AI.

Using Transformers (MinMaxScaler, StandardScaler, RobustScaler)

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Scaling, Image by Flo on OpenSea

This article introduces the centering and scaling concepts.With a real-world use case, I explain the advantages of the center and scale the data.

We dive into simple calculations and explanations by looking at Scikit-Learn ready-made methods.

Technically, we compare the MinMaxScaler, StandardScaler, and RobustScaler. They are part of transformers’ methods facilitating the preprocessing.

By the end, you will understand the purpose of centering and scaling data and be ready to use ready-made Scikit-Learn transformers.

Scaling transforms data to a specific range or scale, while centering involves shifting the data points so that their mean becomes zero.An example below.

Image… Read the full blog for free on Medium.

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