Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Байєсівська гребенева регресія× | Lasso-регресія× | |
|---|---|---|
| Галузь | Машинне навчання | Машинне навчання |
| Родина≠ | Bayesian methods | Machine learning |
| Рік появи≠ | 1992 | 1996 |
| Автор методу≠ | MacKay, D. J. C. | Tibshirani, R. |
| Тип≠ | Probabilistic regularised regression | Regularized linear regression (L1 penalty) |
| Основоположне джерело≠ | 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 ↗ |
| Інші назви | BRR, Bayesian linear regression with automatic relevance determination, evidence approximation ridge, marginal likelihood ridge | LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization |
| Пов'язані≠ | 3 | 4 |
| Підсумок≠ | 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. |
| ScholarGateНабір даних ↗ |
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