পদ্ধতির তুলনা করুন

	অ্যাডাবুস্ট	স্ট্যাকিং
ক্ষেত্র	যন্ত্র শিখন	যন্ত্র শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	1997	1992
প্রবর্তক	Freund, Y. & Schapire, R.E.	Wolpert, D.H.
ধরন	Ensemble (sequential boosting of weak learners)	Ensemble (heterogeneous meta-learning)
মৌলিক উৎস	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.
অপর নাম	AdaBoost (Adaptive Boosting), adaptive boosting, adaptif artırma	Stacking (Yığınlama — Meta-Öğrenme), stacked generalization, meta-learning ensemble, super learner
সম্পর্কিত	5	5
সারসংক্ষেপ	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.