Nested Cross-validation
Nested Cross-Validation is a resampling procedure that employs two hierarchical layers of cross-validation—one for hyperparameter tuning and the other for model evaluation—thereby providing an almost unbiased estimate of a model’s generalization performance while mitigating overfitting in model selection.
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Institutions
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2017–2026
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Machine learning algorithm validation with a limited sample size
Andrius Vabalas, Emma Gowen, Ellen Poliakoff et al. · PLoS ONE · 2019 · 1.7K citations · Full text
Toward more realistic drug-target interaction predictions
Tapio Pahikkala, Antti Airola, Samuli Pietilä et al. · Briefings in Bioinformatics · 2014 · 560 citations · Full text
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Stanford University
Stanford, United States
10 h-index
20 publications
1.8K citations

University of Toronto
Toronto, Canada
7 h-index
16 publications
425 citations
King's College London
London, United Kingdom
6 h-index
19 publications
1.6K citations
University College London
London, United Kingdom
6 h-index
12 publications
363 citations
Universität Hamburg
Hamburg, Germany
5 h-index
19 publications
619 citations
Inserm
Paris, France
4 h-index
23 publications
867 citations

Centre National de la Recherche Scientifique
Paris, France
4 h-index
6 publications
376 citations
Dalhousie University
Halifax, Canada
4 h-index
6 publications
172 citations
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10 publications · 357 citations
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7 publications · 2.1K citations
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