Variable Importance in High-Dimensional Settings Requires Grouping¶
Chamma, A., Thirion, B., & Engemann, D. (2024). Variable Importance in High-Dimensional Settings Requires Grouping. Proceedings of the AAAI Conference on Artificial Intelligence, 38(10), 11195-11203.
Cited by¶
1 citation across 1 artifact.
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Mechanisms¶
- Leakage Ablation Test
- Correlated leakage means removing one proxy can leave a second, redundant one still feeding the answer, so an ablation that shows no drop is not proof of innocence — the signal may simply have rerouted.
This sourceShows that single-variable removal can understate predictive importance when correlated or duplicated variables carry overlapping information, so a null individual ablation is not exculpatory.
- Correlated leakage means removing one proxy can leave a second, redundant one still feeding the answer, so an ablation that shows no drop is not proof of innocence — the signal may simply have rerouted.
Verification¶
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Registry ID ref:22e20ebb5941 · see in the full table