方法对比
并排查看您选择的方法；存在差异的行会高亮显示。
| 贝叶斯岭回归× | 弹性网络 (Elastic Net)× | |
|---|---|---|
| 领域 | 机器学习 | 机器学习 |
| 方法族≠ | Bayesian methods | Machine learning |
| 起源年份≠ | 1992 | 2005 |
| 提出者≠ | MacKay, D. J. C. | Zou, H. & Hastie, T. |
| 类型≠ | Probabilistic regularised regression | Regularized linear regression (L1 + L2 penalty) |
| 开创性文献≠ | MacKay, D. J. C. (1992). Bayesian Interpolation. Neural Computation, 4(3), 415–447. 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 | BRR, Bayesian linear regression with automatic relevance determination, evidence approximation ridge, marginal likelihood ridge | Elastic Net Regresyon, elastic net regression, ElasticNet, L1/L2 regularized regression |
| 相关≠ | 3 | 4 |
| 摘要≠ | Bayesian Ridge Regression is a probabilistic formulation of ridge regression, introduced by David J. C. MacKay in 1992, in which the regularisation strength and noise precision are not fixed by the analyst but are instead estimated automatically by maximising the marginal likelihood (evidence) of the observed data. The result is a full posterior distribution over the regression weights together with calibrated predictive uncertainty. | 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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