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Elastic Net Regularization Paths for All Generalized Linear Models
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DOI：10.18637/jss.v106.i01.png)
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Abstract
En 中文
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.
Keywords:
lasso
elastic net
?1 penalty
regularization path
coordinate descent
generalized linear models
survival
Cox model
Journal
IF:
8.1
Papers:
616
Citations:
4.6W