Comparer des méthodes

	LightGBM Robuste	Régression de Huber
Domaine	Apprentissage automatique	Statistique
Famille	Machine learning	Regression model
Année d'origine	2017 (LightGBM); robust variants widely adopted 2018–present	1964
Auteur d'origine	Ke, G. et al. (LightGBM); robust objectives adapted from Friedman, J. H.	Peter J. Huber
Type	Ensemble (gradient boosted decision trees with robust loss)	Robust linear regression (M-estimation)
Source fondatrice	Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., & Liu, T.-Y. (2017). LightGBM: A Highly Efficient Gradient Boosting Decision Tree. Advances in Neural Information Processing Systems, 30, 3146–3154.	Huber, P. J. (1964). Robust Estimation of a Location Parameter. Annals of Mathematical Statistics, 35(1), 73-101.
Alias	Robust LGBM, LightGBM with Huber loss, outlier-resistant gradient boosting, robust gradient boosted trees	Huber M-estimator, Huber loss regression, robust regression, Huber Regresyonu
Apparentées	6	5
Résumé	Robust LightGBM is a gradient boosting framework that pairs Microsoft's highly efficient LightGBM engine with outlier-resistant loss functions — most commonly Huber, quantile, or mean absolute error — so that predictions are not unduly distorted by extreme or erroneous observations. It retains LightGBM's speed and leaf-wise tree growth while providing resistance to heavy-tailed noise in the target variable.	Huber regression is a robust linear regression method, introduced by Peter J. Huber in 1964, that resists the influence of outliers by treating small and large residuals differently. It applies a squared (OLS-like) loss to small residuals and a milder absolute-value loss to large ones, so extreme observations cannot dominate the fit.