Using the Out-Of-Bag Model in the Cross-Validation Procedure Full article
| Journal |
Pattern Recognition and Image Analysis
ISSN: 1054-6618 , E-ISSN: 1555-6212 |
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| Output data | Year: 2024, Volume: 34, Number: 4, Pages: 1172-1176 Pages count : 5 DOI: 10.1134/S1054661824701232 | ||
| Tags | machine learning, cross-validation, out-of-bag estimation, bias-variance decomposition, overfitting problem | ||
| Authors |
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| Affiliations |
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Funding (1)
| 1 | Sobolev Institute of Mathematics | FWNF-2022-0015 |
Abstract:
In the widely known bagging method (random forest), an out-of-bag estimate is generated, which characterizes the quality of the constructed solution. This paper proposes to transfer the idea of constructing this assessment to the cross-validation procedure, which ultimately comes down to a change in the method for constructing the final solution. The resulting method has a smaller variance component in the corre sponding error decomposition. Another advantage is that the final solution uses the same models that were used to evaluate the quality during the cross-validation process. This can be particularly significant when the classification method uses significant randomization.
Cite:
Nedel'ko V.
Using the Out-Of-Bag Model in the Cross-Validation Procedure
Pattern Recognition and Image Analysis. 2024. V.34. N4. P.1172-1176. DOI: 10.1134/S1054661824701232 WOS РИНЦ OpenAlex
Using the Out-Of-Bag Model in the Cross-Validation Procedure
Pattern Recognition and Image Analysis. 2024. V.34. N4. P.1172-1176. DOI: 10.1134/S1054661824701232 WOS РИНЦ OpenAlex
Dates:
| Submitted: | Apr 20, 2024 |
| Accepted: | Jul 16, 2024 |
| Published print: | Dec 25, 2024 |
| Published online: | Apr 6, 2025 |
Identifiers:
| Web of science: | WOS:001460783600029 |
| Elibrary: | 80615931 |
| OpenAlex: | W4409196990 |
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