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.

翻译：拉索和弹性网是受监督学习流行的常规回归模型。 Friedman、Hastie和Tibshirani(2010年)引入了计算高效的算法,用于计算普通最小回归方、物流回归和多等值物流回归的弹性网络回归路径,而Simon、Friedman、Hastie和Tibshirani(2011年)将这项工作扩展至右检数据的Cox模型。我们进一步将弹性网络回归的覆盖范围扩大到所有通用线性模型家庭、Cox模型(启动、停止)数据和层,以及宽松拉索的简化版。我们还讨论了测量这些适应模型的功能的方便实用功能。