Which Leakage Types Matter? A Quantitative Landscape Across 2,047 Benchmark Datasets
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 ta...
| Authors | Simon Roth |
|---|---|
| Primary category | cs.LG |
| Published | 5 April 2026 |
| Last updated at source | 29 May 2026 |
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