Elastic Net Regularization Paths for All Generalized Linear Models

Elastic Net Regularization Paths for All Generalized Linear Models

: lasso, elastic net, ℓ1 penalty, regularization path, coordinate descent, generalized
linear models, survival, Cox mode

The lasso and elastic net are popular regularized regression models for supervised
learning. Friedman, Hastie, and Tibshirani (2010) introduced a computationally efficient
algorithm for computing the elastic net regularization path for ordinary least squares
regression, logistic regression and multinomial logistic regression, while Simon, Friedman,
Hastie, and Tibshirani (2011) extended this work to Cox models for right-censored data.
We further extend the reach of the elastic net-regularized regression to all generalized
linear model families, Cox models with (start, stop] data and strata, and a simplified
version of the relaxed lasso. We also discuss convenient utility functions for measuring
the performance of these fitted models

J. Kenneth Tay

https://www.jstatsoft.org/article/view/v106i01

Stanford University

March 2023

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