The 'agghoo' procedure is an alternative to usual cross-validation. Instead of choosing the best model trained on V subsamples, it determines a winner model for each subsample, and then aggregates the V outputs. For the details, see "Aggregated hold-out" by Guillaume Maillard, Sylvain Arlot, Matthieu Lerasle (2021) <doi:10.48550/arXiv.1909.04890> published in Journal of Machine Learning Research 22(20):1–55.
Version: 0.1-0 Depends: R (≥ 3.5.0) Imports: class, parallel, R6, rpart, FNN Suggests: roxygen2, mlbench Published: 2023-05-25 DOI: 10.32614/CRAN.package.agghoo Author: Sylvain Arlot [ctb], Benjamin Auder [aut, cre, cph], Melina Gallopin [ctb], Matthieu Lerasle [ctb], Guillaume Maillard [ctb] Maintainer: Benjamin Auder <benjamin.auder at universite-paris-saclay.fr> License: MIT + file LICENSE URL: https://git.auder.net/?p=agghoo.git NeedsCompilation: no Materials: README CRAN checks: agghoo results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=agghoo to link to this page.
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