Compara mètodes

	Boosting Ensemble	AdaBoost	Gradient Boosting	Votació per majoria
Camp	Aprenentatge per conjunts	Aprenentatge automàtic	Aprenentatge automàtic	Aprenentatge per conjunts
Família	Machine learning	Machine learning	Machine learning	Machine learning
Any d'origen	1990	1997	2001	1996
Autor original	Robert Schapire	Freund, Y. & Schapire, R.E.	Friedman, J. H.	Leo Breiman
Tipus	sequential ensemble	Ensemble (sequential boosting of weak learners)	Ensemble (sequential boosting of decision trees)	voting aggregation
Font seminal	Schapire, R. E. (1990). The strength of weak learnability. Machine Learning, 5(2), 197-227.	Freund, Y. & Schapire, R.E. (1997). A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting. Journal of Computer and System Sciences, 55(1), 119–139.	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.
Àlies	adaptive boosting, sequential ensemble	AdaBoost (Adaptive Boosting), adaptive boosting, adaptif artırma	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine	hard voting
Relacionats	4	5	5	5
Resum	Boosting is an ensemble method that sequentially trains weak learners and combines them into a strong predictor by focusing on samples that previous models misclassified. Each new weak learner is weighted according to the difficulty of its training task, and final predictions are made via weighted voting. Pioneered by Schapire (1990) and refined in AdaBoost (Freund & Schapire, 1997), boosting converts weak learners (barely better than random) into strong learners through sequential reweighting.	AdaBoost (Adaptive Boosting) is the original boosting algorithm, introduced by Yoav Freund and Robert Schapire in 1997, that combines a sequence of simple weak learners by giving more weight to the observations they get wrong. The forerunner of gradient boosting, it is simple, interpretable, and a strong baseline for classification.	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.