Порівняння методів

	Бустинг (Ансамбль)	Мажоритарне голосування
Галузь	Ансамблеве навчання	Ансамблеве навчання
Родина	Machine learning	Machine learning
Рік появи	1990	1996
Автор методу	Robert Schapire	Leo Breiman
Тип	sequential ensemble	voting aggregation
Основоположне джерело	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.
Інші назви	adaptive boosting, sequential ensemble	hard voting
Пов'язані	4	5
Підсумок	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.