What are the different scaling methods for machine learning models?

What are the different scaling methods for machine learning models?

There are several scaling methods, including Min-Max Scaling, Standardization (Z-score), and Robust Scaling. Each has its use cases.

Can you explain Min-Max Scaling in more detail?

Min-Max Scaling transforms features to a fixed range, usually [0, 1]. It’s useful when you want to maintain the relationships between data points.

What about Standardization? How does it differ from Min-Max Scaling?

Standardization centers the data around the mean with a unit standard deviation. Unlike Min-Max, it doesn’t bound the values to a specific range.

When should I use Robust Scaling?

Robust Scaling is ideal when your data contains outliers, as it uses the median and interquartile range to scale the data.