Krahasoni metodat

	AdaBoost	Shtresimi
Fusha	Mësimi i makinës	Mësimi i makinës
Familja	Machine learning	Machine learning
Viti i origjinës	1997	1992
Krijuesi	Freund, Y. & Schapire, R.E.	Wolpert, D.H.
Lloji	Ensemble (sequential boosting of weak learners)	Ensemble (heterogeneous meta-learning)
Burimi themelues	Freund, Y. & Schapire, R.E. (1997). A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting. Journal of Computer and System Sciences, 55(1), 119–139.	Wolpert, D.H. (1992). Stacked Generalization. Neural Networks, 5(2), 241–259.
Emërtime të tjera	AdaBoost (Adaptive Boosting), adaptive boosting, adaptif artırma	Stacking (Yığınlama — Meta-Öğrenme), stacked generalization, meta-learning ensemble, super learner
Të lidhura	5	5
Përmbledhja	AdaBoost (Adaptive Boosting) is the original boosting algorithm, introduced by Yoav Freund and Robert Schapire in 1997, that combines a sequence of simple weak learners by giving more weight to the observations they get wrong. The forerunner of gradient boosting, it is simple, interpretable, and a strong baseline for classification.	Stacking, or stacked generalization, is an ensemble method introduced by David Wolpert in 1992 that combines the outputs of several different base models (Level-0) through a separate meta-model (Level-1). Unlike bagging and boosting, it deliberately uses heterogeneous model types, and it is the standard final-stage strategy in Kaggle competitions.