Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Régression Ridge Robuste× | Régression Lasso× | |
|---|---|---|
| Domaine≠ | Statistique | Apprentissage automatique |
| Famille≠ | Regression model | Machine learning |
| Année d'origine≠ | 1991 | 1996 |
| Auteur d'origine≠ | Silvapulle (1991); building on Tikhonov (1963) and Huber (1964) | Tibshirani, R. |
| Type≠ | Regularized robust linear regression | Regularized linear regression (L1 penalty) |
| Source fondatrice≠ | Silvapulle, M. J. (1991). Robust ridge regression based on an M-estimator. Australian Journal of Statistics, 33(3), 319–333. link ↗ | Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288. DOI ↗ |
| Alias | ridge M-estimation, robust regularized regression, M-estimator ridge, outlier-resistant ridge regression | LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization |
| Apparentées≠ | 5 | 4 |
| Résumé≠ | 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. | 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. |
| ScholarGateJeu de données ↗ |
|
|