AI engineering glossary · Feature engineering data prep
What is feature selection?
Feature selection: Choosing a subset of the available features to use in a model, dropping irrelevant or redundant ones. It reduces overfitting, speeds up training, cuts cost and makes models easier to interpret.
Feature selection: an example
From 200 candidate columns, a model keeps the 25 most predictive ones and performs as well as with all 200.
Related terms
More Feature engineering data prep terms
- Feature store
- Feature transformation
- Filter method
- Fit-transform pattern
- Forward selection
- Frequency encoding
- Geospatial features
- Group-aware splitting
Where you learn it
Feature selection is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.