Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Régression linéaire robuste× | Régression Lasso× | |
|---|---|---|
| Domaine | Apprentissage automatique | Apprentissage automatique |
| Famille | Machine learning | Machine learning |
| Année d'origine≠ | 1964–1987 | 1996 |
| Auteur d'origine≠ | Huber, P. J.; Rousseeuw, P. J. | Tibshirani, R. |
| Type≠ | Outlier-resistant supervised regression | Regularized linear regression (L1 penalty) |
| Source fondatrice≠ | Huber, P. J. (1964). Robust Estimation of a Location Parameter. Annals of Mathematical Statistics, 35(1), 73–101. DOI ↗ | Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288. DOI ↗ |
| Alias | robust regression, M-estimator regression, Huber regression, outlier-resistant regression | LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization |
| Apparentées≠ | 5 | 4 |
| Résumé≠ | Robust linear regression fits a linear model between predictors and a continuous outcome while down-weighting or discarding influential outliers, preventing the few anomalous observations that OLS is famously sensitive to from distorting the entire estimated line. Major variants include Huber regression, iteratively reweighted least squares (IRLS), RANSAC, and Theil-Sen estimation. | 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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