Comparer des méthodes

	Gradient Boosting	Vote majoritaire
Domaine	Apprentissage automatique	Apprentissage ensembliste
Famille	Machine learning	Machine learning
Année d'origine	2001	1996
Auteur d'origine	Friedman, J. H.	Leo Breiman
Type	Ensemble (sequential boosting of decision trees)	voting aggregation
Source fondatrice	Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232.	Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123-140.
Alias	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine	hard voting
Apparentées	5	5
Résumé	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.	Majority voting is an ensemble method that combines predictions from multiple base classifiers by selecting the class that receives the most votes. Each base classifier casts one vote for a predicted class, and the final prediction is the class with the majority (plurality). This approach was formalized by Leo Breiman and colleagues in the 1990s as a simple yet effective way to improve classification accuracy.