Bias in Random Forest Variable Importance Measures¶
Strobl, C., Boulesteix, A., Zeileis, A., & Hothorn, T. (2007). Bias in Random Forest Variable Importance Measures: Illustrations, Sources and a Solution. BMC Bioinformatics.
Cited by¶
1 citation across 1 artifact.
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Primes¶
- Cross-Dimensional Leakage
- The corrective is experimental decoupling — permutation or interventional importance that breaks the confound — not a larger training set drawn through the same correlated feature distribution.
This sourceShows permutation/Gini variable-importance is biased toward features correlated with others, so a confounding correlated feature inflates the apparent importance of features that co-vary with it — and that breaking the dependence (conditional permutation) is required to resolve it.
- The corrective is experimental decoupling — permutation or interventional importance that breaks the confound — not a larger training set drawn through the same correlated feature distribution.
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