agghoo: Aggregated Hold-Out Cross Validation

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
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:

Reference manual: agghoo.pdf

Downloads:

Package source: agghoo_0.1-0.tar.gz
Windows binaries: r-devel: agghoo_0.1-0.zip, r-release: agghoo_0.1-0.zip, r-oldrel: agghoo_0.1-0.zip
macOS binaries: r-release (arm64): agghoo_0.1-0.tgz, r-oldrel (arm64): agghoo_0.1-0.tgz, r-release (x86_64): agghoo_0.1-0.tgz

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