Compara mètodes

	Boosting Ensemble	Votació per majoria
Camp	Aprenentatge per conjunts	Aprenentatge per conjunts
Família	Machine learning	Machine learning
Any d'origen	1990	1996
Autor original	Robert Schapire	Leo Breiman
Tipus	sequential ensemble	voting aggregation
Font seminal	Schapire, R. E. (1990). The strength of weak learnability. Machine Learning, 5(2), 197-227.	Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123-140.
Àlies	adaptive boosting, sequential ensemble	hard voting
Relacionats	4	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.	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.