What are the different scaling techniques in machine learning?

What are the different scaling techniques in machine learning?

In machine learning, scaling techniques are used to normalize the range of independent variables. 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, or Z-score normalization, rescales data to have a mean of 0 and a standard deviation of 1, making it useful for algorithms that assume normally distributed data.

When should I use Robust scaling?

Robust scaling is ideal when your data contains outliers. It uses the median and the interquartile range to scale the data, making it less sensitive to outliers.