Cross-validation Stability
Cross-Validation Stability is a methodological metric that quantifies the consistency of model performance estimates across different data partitions in cross-validation procedures, distinguishing itself by assessing the robustness of predictive accuracy to variations in training and validation subsets.
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2017–2026
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Jose G. Moreno-Torres, José A. Sáez, Francisco Herrera · IEEE Transactions on Neural Networks and Learning Systems · 2012 · 301 citations
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Harris–Stowe State University
St Louis, United States
1 h-index
1 publications
19 citations
Yahoo (United Kingdom)
London, United Kingdom
1 h-index
3 publications
96 citations
National Academies of Sciences, Engineering, and Medicine
Washington, United States
1 h-index
1 publications
15 citations

University of Aizu
Fukushima, Japan
1 h-index
1 publications
25 citations
Clinical Research Institute
Plymouth, United States
1 h-index
1 publications
103 citations
University of Copenhagen
Copenhagen, Denmark
1 h-index
2 publications
700 citations
IBM (United States)
Armonk, United States
1 h-index
1 publications
96 citations

Columbia University Irving Medical Center
New York, United States
1 h-index
1 publications
103 citations
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JCGS · 2 publications · 147 citations
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2 publications · 433 citations
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JETAI · 1 publications · 15 citations
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1 publications · 171 citations
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1 publications · 19 citations
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