Сравнение методов

	Стекинг (Stacked Generalization)	Ансамбль бэггинга	Ансамбль бустинга	Мажоритарное голосование
Область	Ансамблевое обучение	Ансамблевое обучение	Ансамблевое обучение	Ансамблевое обучение
Семейство	Machine learning	Machine learning	Machine learning	Machine learning
Год появления	1992	1996	1990	1996
Автор метода	David Wolpert	Leo Breiman	Robert Schapire	Leo Breiman
Тип	meta-learning aggregation	parallel ensemble	sequential ensemble	voting aggregation
Основополагающий источник	Wolpert, D. H. (1992). Stacked generalization. Neural Networks, 5(2), 241-259.	Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123-140.	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.
Другие названия	stacking, meta-learning	bootstrap aggregating	adaptive boosting, sequential ensemble	hard voting
Связанные	3	4	4	5
Сводка	Stacked generalization, or stacking, is a two-level ensemble method where base-level classifiers are trained on the original data, and a meta-learner is trained on the predictions of the base classifiers. The meta-learner learns how to best combine base predictions rather than using fixed aggregation rules. Introduced by David Wolpert in 1992, stacking achieves state-of-the-art performance by automatically learning the optimal weighting and interaction patterns among base models.	Bagging, short for bootstrap aggregating, is an ensemble method that reduces variance by training multiple copies of a single learning algorithm on different random subsets of the training data. Each subset is created via bootstrap sampling—randomly drawing samples with replacement. Predictions are combined through majority voting (classification) or averaging (regression). Introduced by Leo Breiman in 1996, bagging forms the foundation for random forests and is particularly effective for reducing overfitting in high-variance models.	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.