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Feature importance methods for linear regression model. / Лю, Цзин; Чжао, Чи; Парилина, Елена Михайловна.

In: Applied Intelligence, Vol. 56, 332, 06.07.2026.

Research output: Contribution to journal › Article › peer-review

Harvard

Лю, Ц, Чжао, Ч & Парилина, ЕМ 2026, 'Feature importance methods for linear regression model', Applied Intelligence, vol. 56, 332. https://doi.org/10.1007/s10489-026-07342-7

APA

Лю, Ц., Чжао, Ч., & Парилина, Е. М. (2026). Feature importance methods for linear regression model. Applied Intelligence, 56, [332]. https://doi.org/10.1007/s10489-026-07342-7

Vancouver

Лю Ц, Чжао Ч, Парилина ЕМ. Feature importance methods for linear regression model. Applied Intelligence. 2026 Jul 6;56. 332. https://doi.org/10.1007/s10489-026-07342-7

Author

Лю, Цзин ; Чжао, Чи ; Парилина, Елена Михайловна. / Feature importance methods for linear regression model. In: Applied Intelligence. 2026 ; Vol. 56.

BibTeX

@article{b5e10638d8654e01bb42b9b6d7550510,
title = "Feature importance methods for linear regression model",
author = "Цзин Лю and Чи Чжао and Парилина, {Елена Михайловна}",
year = "2026",
month = jul,
day = "6",
doi = "10.1007/s10489-026-07342-7",
language = "English",
volume = "56",
journal = "Applied Intelligence",
issn = "0924-669X",
publisher = "Springer Nature",

}

RIS

TY - JOUR

T1 - Feature importance methods for linear regression model

AU - Лю, Цзин

AU - Чжао, Чи

AU - Парилина, Елена Михайловна

PY - 2026/7/6

Y1 - 2026/7/6

UR - https://link.springer.com/article/10.1007/s10489-026-07342-7

U2 - 10.1007/s10489-026-07342-7

DO - 10.1007/s10489-026-07342-7

M3 - Article

VL - 56

JO - Applied Intelligence

JF - Applied Intelligence

SN - 0924-669X

M1 - 332

ER -

ID: 156890892