Módszerek összehasonlítása

	Stacked Generalization	Bagging Ensemble	Többségi szavazás
Tudományterület	Együttes tanulás	Együttes tanulás	Együttes tanulás
Módszercsalád	Machine learning	Machine learning	Machine learning
Keletkezés éve	1992	1996	1996
Megalkotó	David Wolpert	Leo Breiman	Leo Breiman
Típus	meta-learning aggregation	parallel ensemble	voting aggregation
Alapmű	Wolpert, D. H. (1992). Stacked generalization. Neural Networks, 5(2), 241-259.	Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123-140.	Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123-140.
Alternatív nevek	stacking, meta-learning	bootstrap aggregating	hard voting
Kapcsolódó	3	4	5
Összefoglaló	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.	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.