AI engineering glossary · Feature engineering data prep
What is group-aware splitting?
Group-aware splitting (also called GroupKFold): Splitting data so that all rows belonging to one group, such as a patient or customer, fall entirely in either train or test. It prevents leakage caused by the same entity appearing on both sides.
Group-aware splitting: an example
With several scans per patient, all scans of one patient go to the same fold, so the model is tested on unseen patients.
Related terms
More Feature engineering data prep terms
- Hashing trick
- Hashing vectorizer
- Haversine distance
- High-cardinality feature
- Hot-deck imputation
- Information value
- Interaction feature
- Interpolation
Where you learn it
Group-aware splitting is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.