Cross-validation
Cross-Validation is a model evaluation technique in predictive modeling that systematically partitions data into complementary subsets, using one portion for training and the remaining for validation, to assess generalization performance while minimizing overfitting.
9.7K
Publications
668.2K
Citations
38.2K
Authors
7.5K
Institutions
Publications per year
2017–2026
5K
Parents
9.7K
A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection
Ron Kohavi · 1995 · 10.7K citations · Full text
A survey of cross-validation procedures for model selection
2009 · 3.8K citations
1–5 of 9.7K
38.2K
Xinjiang Technical Institute of Physics & Chemistry, China
52 publications · 3.3K citations
30
h-index
25
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University of Electronic Science and Technology of China, China
25 publications · 2.9K citations
24
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22
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21
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1–5 of 38.2K
7.5K

Chinese Academy of Sciences
Beijing, China
56 h-index
423 publications
26.6K citations

University of Electronic Science and Technology of China
Chengdu, China
45 h-index
220 publications
21.2K citations
Stanford University
Stanford, United States
44 h-index
189 publications
43K citations
University of Wisconsin–Madison
Madison, United States
41 h-index
207 publications
20K citations
Harvard University
Cambridge, United States
40 h-index
255 publications
15.4K citations
Columbia University
New York, United States
37 h-index
83 publications
8.8K citations
University of California, Berkeley
Berkeley, United States
36 h-index
107 publications
23.8K citations
China University of Mining and Technology
Xuzhou, China
36 h-index
146 publications
10.9K citations
1–8 of 7.5K
145 publications · 24K citations
75
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234 publications · 12.2K citations
61
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203 publications · 13.4K citations
58
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148 publications · 12.2K citations
55
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152 publications · 8.5K citations
53
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1–5