Sammenlign metoder

	Flertalsafstemning	Stablet Generalisering
Fagområde	Ensemblelæring	Ensemblelæring
Familie	Machine learning	Machine learning
Oprindelsesår	1996	1992
Ophavsperson	Leo Breiman	David Wolpert
Type	voting aggregation	meta-learning aggregation
Oprindelig kilde	Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123-140.	Wolpert, D. H. (1992). Stacked generalization. Neural Networks, 5(2), 241-259.
Aliasser	hard voting	stacking, meta-learning
Relaterede	5	3
Resumé	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.	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.