Hold out entire groups to evaluate generalization to unseen groups
When the evaluation target is prediction for unseen groups, every group represented in a validation fold must be absent from its paired training fold. In scikit-learn, GroupKFold implements this separation using sample group identifiers supplied through the groups parameter. This controls training–validation group overlap for that evaluation target.
Conditions and limits:
- Predicting future observations from already represented groups is a different evaluation target.
- The number of distinct groups must be at least the number of folds; each group appears in a test fold exactly once across the folds.
- Group separation does not guarantee preserved class proportions, equal sample counts or chronological order.
- Correct group identifiers are needed to enforce the intended separation.
- Controlling group overlap does not establish that preprocessing, feature or target leakage has been eliminated.
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Hold out entire groups to evaluate generalization to unseen groups
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