Gespeichert in:
| 1. Verfasser: | |
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2604.04199 |
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Inhaltsangabe:
- Twenty-eight within-subject counterfactual experiments across 2,047 iid tabular datasets, plus a boundary experiment on 129 temporal datasets, measure the severity of four data leakage classes in machine learning. Class I (estimation: fitting scalers on full data) is negligible: all nine conditions produce $|ΔAUC| \leq 0.005$. Class II (selection: peeking, seed cherry-picking) is substantial: the measured effect is consistent with about 90% noise exploitation inflating reported scores. Class III (memorization) scales with model capacity: $d_z$ = 0.37 (Naive Bayes) to 1.11 (Decision Tree) at 10% duplication. Class IV (boundary) is invisible under random cross-validation. Within this iid tabular regime, the textbook emphasis is inverted: normalization leakage matters least; selection leakage at practical dataset sizes matters most.