What are the different methods of feature scaling in machine learning?

What are the different methods of feature scaling in machine learning?

Feature scaling is crucial for algorithms that rely on distance metrics. Common methods include Min-Max scaling, Standardization (Z-score normalization), and Robust scaling.

Can you explain Min-Max scaling?

Min-Max scaling transforms features to a fixed range, usually [0, 1]. It subtracts the minimum value and divides by the range of the feature.

What about Standardization?

Standardization centers the data around the mean with a unit standard deviation. It’s calculated as (X - mean) / std deviation.

When should I use Robust scaling?

Robust scaling is useful when your data contains outliers. It uses the median and the interquartile range for scaling.