方法对比
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| 贝叶斯岭回归× | Lasso 回归× | |
|---|---|---|
| 领域 | 机器学习 | 机器学习 |
| 方法族≠ | Bayesian methods | Machine learning |
| 起源年份≠ | 1992 | 1996 |
| 提出者≠ | MacKay, D. J. C. | Tibshirani, R. |
| 类型≠ | Probabilistic regularised regression | Regularized linear regression (L1 penalty) |
| 开创性文献≠ | MacKay, D. J. C. (1992). Bayesian Interpolation. Neural Computation, 4(3), 415–447. DOI ↗ | Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288. DOI ↗ |
| 别名 | BRR, Bayesian linear regression with automatic relevance determination, evidence approximation ridge, marginal likelihood ridge | LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization |
| 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. | 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. |
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