Sammenlign metoder

	Majoritetsstemmegivning	Stablet generalisering
Fagfelt	Ensemblelæring	Ensemblelæring
Familie	Machine learning	Machine learning
Opprinnelsesår	1996	1992
Opphavsperson	Leo Breiman	David Wolpert
Type	voting aggregation	meta-learning aggregation
Opprinnelig kilde	Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123-140.	Wolpert, D. H. (1992). Stacked generalization. Neural Networks, 5(2), 241-259.
Alias	hard voting	stacking, meta-learning
Relaterte	5	3
Sammendrag	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.