Elastic Net Regression
Elastic Net Regression is a regularized linear regression method that combines both L1 and L2 penalty terms to balance sparsity and multicollinearity by simultaneously performing variable selection and coefficient shrinkage.
669
Publications
78.7K
Citations
3.1K
Authors
1.2K
Institutions
Publications per year
2017–2026
471
Parents
669
Pathwise coordinate optimization
Jerome H. Friedman, Trevor Hastie, Holger Höfling et al. · The Annals of Applied Statistics · 2007 · 1.9K citations · Full text
1–5 of 669
3.1K
10
h-index
8
h-index
5
h-index
Universidade Federal do Rio Grande do Sul, Brazil
5 publications · 174 citations
5
h-index
4
h-index
1–5 of 3.1K
1.2K
Stanford University
Stanford, United States
20 h-index
73 publications
76K citations
Harvard University
Cambridge, United States
20 h-index
102 publications
4.4K citations

University of Minnesota
Minneapolis, United States
13 h-index
29 publications
1.3K citations
University of Michigan
Ann Arbor, United States
12 h-index
31 publications
2.7K citations

Brigham and Women's Hospital
Boston, United States
12 h-index
40 publications
1.4K citations
University College London
London, United Kingdom
12 h-index
30 publications
901 citations
King's College London
London, United Kingdom
10 h-index
57 publications
5.9K citations
Duke University
Durham, United States
10 h-index
35 publications
8.9K citations
1–8 of 1.2K
18 publications · 935 citations
17
h-index
17 publications · 770 citations
16
h-index
11 publications · 668 citations
11
h-index
Repository · 10 publications · 658 citations
10
h-index
10 publications · 773 citations
10
h-index
1–5