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.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.1-0 |
rolling linux/jammy R-4.5 | agghoo_0.1-0.tar.gz |
121.2 KiB |
0.1-0 |
rolling linux/noble R-4.5 | agghoo_0.1-0.tar.gz |
121.4 KiB |
0.1-0 |
rolling source/ R- | agghoo_0.1-0.tar.gz |
11.7 KiB |
0.1-0 |
latest linux/jammy R-4.5 | agghoo_0.1-0.tar.gz |
121.2 KiB |
0.1-0 |
latest linux/noble R-4.5 | agghoo_0.1-0.tar.gz |
121.4 KiB |
0.1-0 |
latest source/ R- | agghoo_0.1-0.tar.gz |
11.7 KiB |
0.1-0 |
2026-04-26 source/ R- | agghoo_0.1-0.tar.gz |
11.7 KiB |
0.1-0 |
2026-04-23 source/ R- | agghoo_0.1-0.tar.gz |
11.7 KiB |
0.1-0 |
2026-04-09 windows/windows R-4.5 | agghoo_0.1-0.zip |
124.0 KiB |
0.1-0 |
2025-04-20 source/ R- | agghoo_0.1-0.tar.gz |
11.7 KiB |