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Using the Out-Of-Bag Model in the Cross-Validation Procedure Научная публикация

Журнал Pattern Recognition and Image Analysis
ISSN: 1054-6618 , E-ISSN: 1555-6212
Вых. Данные Год: 2024, Том: 34, Номер: 4, Страницы: 1172-1176 Страниц : 5 DOI: 10.1134/S1054661824701232
Ключевые слова machine learning, cross-validation, out-of-bag estimation, bias-variance decomposition, overfitting problem
Авторы Nedel'ko V. 1
Организации
1 Sobolev Institute of Mathematics of the Siberian Branch of the Russian Academy of Sciences

Информация о финансировании (1)

1 Институт математики им. С.Л. Соболева СО РАН FWNF-2022-0015

Реферат: 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.
Библиографическая ссылка: 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
Даты:
Поступила в редакцию: 20 апр. 2024 г.
Принята к публикации: 16 июл. 2024 г.
Опубликована в печати: 25 дек. 2024 г.
Опубликована online: 6 апр. 2025 г.
Идентификаторы БД:
Web of science: WOS:001460783600029
РИНЦ: 80615931
OpenAlex: W4409196990
Цитирование в БД: Пока нет цитирований
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