Machine learning
弹性网络 (Elastic Net)
弹性网络是由Zou和Hastie于2005年提出的一种正则化线性回归方法，它结合了LASSO (L1) 和 Ridge (L2) 惩罚项，能够同时进行变量选择和系数收缩。它适用于具有许多可能相关的预测变量的数据的预测和解释建模。
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来源
- Zou, H. & Hastie, T. (2005). Regularization and Variable Selection via the Elastic Net. Journal of the Royal Statistical Society: Series B, 67(2), 301–320. DOI: 10.1111/j.1467-9868.2005.00503.x ↗
如何引用本页
ScholarGate. (2026, June 1). Elastic Net Regularized Regression. ScholarGate. https://scholargate.app/zh/machine-learning/elastic-net
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