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- Stratified sampling ensures each subset (train/test/validation) contains the same proportion of each class as the original dataset. This is critical for imbalanced datasets — random sampling could produce a test set with very few minority-class examples, distorting metrics. Bootstrap sampling draws with replacement and is used for ensembles like random forests, not strictly for class preservation.