2024-04-18-benard24a.md

title	software	abstract	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
MMD-based Variable Importance for Distributional Random Forest	https://github.com/JeffNaef/DRFvarimportance	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 conditional output distributions.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	benard24a	0	{MMD}-based Variable Importance for Distributional Random Forest	1324	1332	1324-1332	1324	false	B\'{e}nard, Cl\'{e}ment and N\"{a}f, Jeffrey and Josse, Julie	given family Clément Bénard given family Jeffrey Näf given family Julie Josse	2024-04-18		Proceedings of The 27th International Conference on Artificial Intelligence and Statistics	238	inproceedings	date-parts 2024 4 18	https://proceedings.mlr.press/v238/benard24a/benard24a.pdf