Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Beiziešu Rīdža regresija× | Elastic Net× | |
|---|---|---|
| Nozare | Mašīnmācīšanās | Mašīnmācīšanās |
| Saime≠ | Bayesian methods | Machine learning |
| Izcelsmes gads≠ | 1992 | 2005 |
| Autors≠ | MacKay, D. J. C. | Zou, H. & Hastie, T. |
| Tips≠ | Probabilistic regularised regression | Regularized linear regression (L1 + L2 penalty) |
| Pirmavots≠ | 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 ↗ |
| Citi nosaukumi | 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 |
| Saistītās≠ | 3 | 4 |
| Kopsavilkums≠ | 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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