Elastic Net Regularization Paths for All Generalized Linear Models

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…

• NS
  National Science Foundation

  Awards: 2013736, IIS 1837931, 5R01 EB 001988-21, DMS-2013736
• NI
  National Institutes of Health

  Awards: 5UL1TR003142, 5R01 EB 001988-21
• NC
  National Center for Advancing Translational Sciences

  Award: 5UL1TR003142