Comparar métodos

	Ensemble de Boosting	Votación Mayoritaria
Campo	Aprendizaje por conjuntos	Aprendizaje por conjuntos
Familia	Machine learning	Machine learning
Año de origen	1990	1996
Autor original	Robert Schapire	Leo Breiman
Tipo	sequential ensemble	voting aggregation
Fuente 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.
Alias	adaptive boosting, sequential ensemble	hard voting
Relacionados	4	5
Resumen	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.