Tay J Kenneth, Narasimhan Balasubramanian, Hastie Trevor
Department of Statistics, Stanford University, 390 Jane Stanford Way, Stanford, California 94305, United States of America.
Department of Biomedical Data Sciences, and Department of Statistics, Stanford University, 390 Jane Stanford Way, Stanford, CA 94305.
J Stat Softw. 2023;106. doi: 10.18637/jss.v106.i01. Epub 2023 Mar 23.
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.
套索和弹性网络是用于监督学习的流行正则化回归模型。弗里德曼、哈斯蒂和蒂布希拉尼（2010年）介绍了一种计算效率高的算法，用于计算普通最小二乘回归、逻辑回归和多项逻辑回归的弹性网络正则化路径，而西蒙、弗里德曼、哈斯蒂和蒂布希拉尼（2011年）将这项工作扩展到了用于右删失数据的考克斯模型。我们进一步将弹性网络正则化回归的适用范围扩展到所有广义线性模型族、具有（起始，终止]数据和分层的考克斯模型，以及松弛套索的简化版本。我们还讨论了用于衡量这些拟合模型性能的便捷实用函数。
