Comparer des méthodes

	LightGBM Robuste	Gradient Boosting
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2017 (LightGBM); robust variants widely adopted 2018–present	2001
Auteur d'origine	Ke, G. et al. (LightGBM); robust objectives adapted from Friedman, J. H.	Friedman, J. H.
Type	Ensemble (gradient boosted decision trees with robust loss)	Ensemble (sequential boosting of decision trees)
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.	Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232.
Alias	Robust LGBM, LightGBM with Huber loss, outlier-resistant gradient boosting, robust gradient boosted trees	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine
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.	Gradient Boosting is an ensemble learning method, formalised by Jerome H. Friedman in 2001, that combines a sequence of weak learners — typically shallow decision trees — so that each new tree is fitted to minimise the residual errors of the trees before it. It is the core algorithm behind popular implementations such as XGBoost, LightGBM and CatBoost.