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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 |
| Reference manual: | agghoo.pdf |
| 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, r-oldrel (x86_64): agghoo_0.1-0.tgz |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.