2 Title: Aggregated Hold-Out Cross Validation
5 Description: The 'agghoo' procedure is an alternative to usual cross-validation.
6 Instead of choosing the best model trained on V subsamples, it determines
7 a winner model for each subsample, and then aggregate the V outputs.
8 For the details, see "Aggregated hold-out" by Guillaume Maillard,
9 Sylvain Arlot, Matthieu Lerasle (2021) <arXiv:1909.04890>
10 published in Journal of Machine Learning Research 22(20):1--55.
11 Author: Sylvain Arlot <sylvain.arlot@universite-paris-saclay.fr> [cph,ctb],
12 Benjamin Auder <benjamin.auder@universite-paris-saclay.fr> [aut,cre,cph],
13 Melina Gallopin <melina.gallopin@universite-paris-saclay.fr> [cph,ctb],
14 Matthieu Lerasle <matthieu.lerasle@universite-paris-saclay.fr> [cph,ctb],
15 Guillaume Maillard <guillaume.maillard@uni.lu> [cph,ctb]
16 Maintainer: Benjamin Auder <benjamin.auder@universite-paris-saclay.fr>
27 URL: https://git.auder.net/?p=agghoo.git
28 License: MIT + file LICENSE