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| Solidna regresja grzbietowa× | Regularyzacja grzbietowa (Ridge Regression)× | |
|---|---|---|
| Dziedzina≠ | Statystyka | Uczenie maszynowe |
| Rodzina≠ | Regression model | Machine learning |
| Rok powstania≠ | 1991 | 1970 |
| Twórca≠ | Silvapulle (1991); building on Tikhonov (1963) and Huber (1964) | Hoerl, A.E. & Kennard, R.W. |
| Typ≠ | Regularized robust linear regression | L2-regularized linear regression |
| Źródło pierwotne≠ | Silvapulle, M. J. (1991). Robust ridge regression based on an M-estimator. Australian Journal of Statistics, 33(3), 319–333. link ↗ | Hoerl, A.E. & Kennard, R.W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67. DOI ↗ |
| Inne nazwy | ridge M-estimation, robust regularized regression, M-estimator ridge, outlier-resistant ridge regression | Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization |
| Pokrewne≠ | 5 | 4 |
| Podsumowanie≠ | Robust Ridge regression combines M-estimation with L2 (ridge) regularization to produce coefficient estimates that are simultaneously resistant to outliers and stable under multicollinearity. It minimizes a robust loss function (such as Huber's) penalized by the squared norm of the coefficient vector, downweighting influential observations while shrinking correlated predictors toward zero. | Ridge Regression is an L2-regularized linear regression method, introduced by Arthur Hoerl and Robert Kennard in 1970, that reduces multicollinearity by adding a penalty on the size of the coefficients. It shrinks coefficients toward zero without setting any of them exactly to zero, producing more stable estimates when predictors are highly correlated. |
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