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.
What is Centering and Scaling?
Understand the concepts
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.
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You can see the effect of scaling and centering the data. Now, at the right, data are centered around 0 and appear on a shorter scale (axes X and Y).
Advantages
There are several advantages to center and scaling the data, the most important for scaling