When should I use standardization instead of min-max scaling?

What’s the difference between standardization and min-max scaling?

Standardization scales data to have a mean of 0 and a standard deviation of 1, while min-max scaling rescales data to a range of [0, 1].

When should I use standardization?

Use standardization when your data follows a Gaussian distribution or when you want to compare scores across different scales.

And when is min-max scaling more appropriate?

Min-max scaling is ideal when you need to bound your data within a specific range, especially for algorithms sensitive to the scale.

Can you give examples of algorithms that require standardization?

Sure! Algorithms like Support Vector Machines, K-means clustering, and Principal Component Analysis benefit from standardization.