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Normalization In Machine Learning

This article explains normalization in Machine Learning.

What Is Normalization?

Normalization is the process of scaling numeric data so that all values fall within a specific range, typically 0 to 1.

Min–Max Normalization is a technique used to rescale numerical data to a fixed range, usually 0 to 1. The formula is: -

Where:

Consider the following exam scores.

[40, 60, 80, 100]

Minimum value is 40, and maximum value is 100.

Max-Abs Normalization scales numerical data by dividing each value by the maximum absolute value in the dataset. Consider the following example: -

[−10,−5,0,5,10]

The normative value of each value is: -

Mean Normalization scales numerical data by subtracting the mean and dividing by the range of the feature.

Consider the dataset:

[10, 20, 30, 40, 50]

The mean of the above dataset is: -

(10 + 20 + 30 + 40 + 50)/5 = 30

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