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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) <arXiv:1909.04890> published in Journal of Machine Learning Research 22(20):1--55.
Getting started
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Package details |
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| Author | Sylvain Arlot [ctb], Benjamin Auder [aut, cre, cph], Melina Gallopin [ctb], Matthieu Lerasle [ctb], Guillaume Maillard [ctb] |
| Maintainer | Benjamin Auder <benjamin.auder@universite-paris-saclay.fr> |
| License | MIT + file LICENSE |
| Version | 0.1-0 |
| URL | https://git.auder.net/?p=agghoo.git |
| Package repository | View on CRAN |
| Installation |
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