<p>MinSizeML: An R package to estimate the minimum sample size in supervised learning for classification</p>

Guillermo Prol Castelo; Jose Luis Mosquera

doi:10.7490/f1000research.1119532.1

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Prol Castelo G and Mosquera JL. MinSizeML: An R package to estimate the minimum sample size in supervised learning for classification [version 1; not peer reviewed]. F1000Research 2023, 12:835 (poster) (https://doi.org/10.7490/f1000research.1119532.1)

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MinSizeML: An R package to estimate the minimum sample size in supervised learning for classification

Guillermo Prol Castelo, Jose Luis Mosquera¹

Published 15 Jul 2023 (https://doi.org/10.7490/f1000research.1119532.1)

Author Affiliations

¹ Faculty of Computer Science, Multimedia and Telecommunications, Open University of Catalonia, Barcelona, Catalonia, Spain

Copyright © 2023 Prol Castelo G and Mosquera JL. This is an open access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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CITE

Published 15 Jul 2023

MinSizeML: An R package to estimate the minimum sample size in supervised learning for classification
[version 1; not peer reviewed]

Guillermo Prol Castelo, Jose Luis Mosquera¹

Author Affiliations

¹ Faculty of Computer Science, Multimedia and Telecommunications, Open University of Catalonia, Barcelona, Catalonia, Spain

Presented at

European Conference on Computational Biology (ECCB) 2022

Abstract

Competing Interests

No competing interests were disclosed

Keywords

Sample Size Estimation, Machine Learning, Comparison of Algorithms, Learning Curve

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