AI engineering glossary · Feature engineering data prep
What is forward selection?
Forward selection (also called Forward feature selection): A greedy wrapper method that begins with no features and adds, one at a time, the feature that most improves the model, stopping when improvement ends. It is simple but can miss combinations that only work together.
Forward selection: an example
Start with an empty model, add 'distance' because it helps most, then 'hour', and stop when adding more gives no gain.
Related terms
More Feature engineering data prep terms
- Frequency encoding
- Geospatial features
- Group-aware splitting
- Hashing trick
- Hashing vectorizer
- Haversine distance
- High-cardinality feature
- Hot-deck imputation
Where you learn it
Forward 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.