AI engineering glossary · Feature engineering data prep
What is feature scaling?
Feature scaling: Rescaling numeric features so they sit on comparable ranges. Models that use distances or gradients, such as k-nearest neighbours, SVMs and neural networks, are distorted when one feature is measured in lakhs and another in single digits. Scaling puts them on equal footing and speeds up gradient-based training.
Feature scaling: an example
Age (18 to 60) and annual salary (2,00,000 to 40,00,000) are both rescaled, so salary does not swamp age when distances between customers are computed.
Related terms
More Feature engineering data prep terms
- Feature selection
- Feature store
- Feature transformation
- Filter method
- Fit-transform pattern
- Forward selection
- Frequency encoding
- Geospatial features
Where you learn it
Feature scaling is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.