Machine learningMachine learning
正则化线性回归
正则化线性回归在普通最小二乘目标函数中增加了一个惩罚项，通过收缩或将系数置零来减少过拟合并处理多重共线性。三种主要变体——Ridge（L2惩罚）、Lasso（L1惩罚）和Elastic Net（L1+L2组合）——使得线性回归即使在特征数量多于观测数量或预测变量高度相关时也能使用。
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来源
- Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288. DOI: 10.1111/j.2517-6161.1996.tb02080.x ↗
- Hastie, T., Tibshirani, R. & Friedman, J. (2009). The Elements of Statistical Learning (2nd ed., Ch. 3). Springer. ISBN: 978-0-387-84858-7
如何引用本页
ScholarGate. (2026, June 3). Regularized Linear Regression (Ridge, Lasso, Elastic Net). ScholarGate. https://scholargate.app/zh/machine-learning/regularized-linear-regression
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