MMD-based Variable Importance for Distributional Random Forest - Archive ouverte HAL
Conference Papers Year : 2024

MMD-based Variable Importance for Distributional Random Forest

Abstract

Distributional Random Forest (DRF) is a flexible forest-based method to estimate the full conditional distribution of a multivariate output of interest given input variables. In this article, we introduce a variable importance algorithm for DRFs, based on the well-established drop and relearn principle and MMD distance. While traditional importance measures only detect variables with an influence on the output mean, our algorithm detects variables impacting the output distribution more generally. We show that the introduced importance measure is consistent, exhibits high empirical performance on both real and simulated data, and outperforms competitors. In particular, our algorithm is highly efficient to select variables through recursive feature elimination, and can therefore provide small sets of variables to build accurate estimates of condi- tional output distributions.
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Dates and versions

hal-04251588 , version 1 (20-10-2023)

Identifiers

  • HAL Id : hal-04251588 , version 1

Cite

Clément Bénard, Jeffrey Naf, Julie Josse. MMD-based Variable Importance for Distributional Random Forest. AISTATS 2024 - The 27th International Conference on Artificial Intelligence and Statistics, May 2024, Valence, Spain. ⟨hal-04251588⟩
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