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1Package: agghoo
2Title: Aggregated Hold-out Cross Validation
3Date: 2021-06-05
4Version: 0.1-0
5Description: The 'agghoo' procedure is an alternative to usual cross-validation.
6 Instead of choosing the best model trained on V subsamples, it determines
7 a winner model for each subsample, and then aggregate the V outputs.
8 For the details, see "Aggregated hold-out" by Guillaume Maillard,
9 Sylvain Arlot, Matthieu Lerasle (2021) <arXiv:1909.04890>
10 published in Journal of Machine Learning Research 22(20):1--55.
11Author: Sylvain Arlot <sylvain.arlot@universite-paris-saclay.fr> [cph,ctb],
12 Benjamin Auder <benjamin.auder@universite-paris-saclay.fr> [aut,cre,cph],
13 Melina Gallopin <melina.gallopin@universite-paris-saclay.fr> [cph,ctb],
14 Matthieu Lerasle <matthieu.lerasle@universite-paris-saclay.fr> [cph,ctb],
15 Guillaume Maillard <guillaume.maillard@uni.lu> [cph,ctb]
16Maintainer: Benjamin Auder <benjamin.auder@universite-paris-saclay.fr>
17Depends:
18 R (>= 3.5.0)
19Imports:
20 R6,
21 caret,
22 rpart,
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23 randomForest,
24 FNN
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25Suggests:
26 roxygen2
27URL: https://git.auder.net/?p=agghoo.git
28License: MIT + file LICENSE
29RoxygenNote: 7.1.1
30Collate:
31 'agghoo.R'
cca5f1c6 32 'R6_AgghooCV.R'
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33 'R6_Model.R'
34 'A_NAMESPACE.R'