方法对比
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| Lasso 回归× | 弹性网络 (Elastic Net)× | |
|---|---|---|
| 领域 | 机器学习 | 机器学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 1996 | 2005 |
| 提出者≠ | Tibshirani, R. | Zou, H. & Hastie, T. |
| 类型≠ | Regularized linear regression (L1 penalty) | Regularized linear regression (L1 + L2 penalty) |
| 开创性文献≠ | Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288. DOI ↗ | 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 ↗ |
| 别名 | LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization | Elastic Net Regresyon, elastic net regression, ElasticNet, L1/L2 regularized regression |
| 相关 | 4 | 4 |
| 摘要≠ | Lasso regression, introduced by Robert Tibshirani in 1996, is a linear regression method that adds an L1 penalty to the loss so that it shrinks coefficients and performs variable selection at the same time, producing a sparse model. By driving some coefficients exactly to zero it keeps only the predictors that matter. | Elastic Net is a regularized linear regression method introduced by Zou and Hastie in 2005 that blends the LASSO (L1) and Ridge (L2) penalties, so it performs variable selection and coefficient shrinkage at the same time. It is designed for predictive and explanatory modelling on data with many, possibly correlated, predictors. |
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