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:
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