Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Beiziešu Rīdža regresija× | LASSO regresija× | |
|---|---|---|
| Nozare | Mašīnmācīšanās | Mašīnmācīšanās |
| Saime≠ | Bayesian methods | Machine learning |
| Izcelsmes gads≠ | 1992 | 1996 |
| Autors≠ | MacKay, D. J. C. | Tibshirani, R. |
| Tips≠ | Probabilistic regularised regression | Regularized linear regression (L1 penalty) |
| Pirmavots≠ | 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 ↗ |
| Citi nosaukumi | BRR, Bayesian linear regression with automatic relevance determination, evidence approximation ridge, marginal likelihood ridge | LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization |
| 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. | 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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