Towards AI Can Help your Team Adopt AI: Corporate Training, Consulting, and Talent Solutions.


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)

Top highlight

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.

Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.

Published via Towards AI

Feedback ↓