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Nested cross-validation when selecting classifiers is overzealous for most practical applications
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Metadata
- Authors
- Wainer, J., & Cawley, G.
- Affiliations
- Universidade Estadual de Campinas (UNICAMP) (BR)
- University of East Anglia (GB)
- Corresponding author
- Wainer, Jacques
- Venue
- Expert Systems with Applications
- Publisher
- Elsevier BV
- Published
- 2021-11
- Volume
- 182
- Pages
- 115222
- ISSN
- 0957-4174 (ISSN-L)
- OpenAlex
- W2892606308
- Cited by
- 309
- References
- 25
- Citations by year
- 2026: 63 · 2025: 64 · 2024: 68 · 2023: 46 · 2022: 40 · 2021: 14
- FWCI
- 14.93
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Expert Systems with Applications (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- UEA Digital Repository (University of East Anglia) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Physical Sciences › Computer Science › Information Systems › Data Mining and Machine Learning Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Imbalanced Data Classification Techniques
- Keywords
- nested cross-validation, classifier selection, hyperparameter optimization, algorithm selection, binary classification, bias-variance trade-off, computational cost
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