Порівняння методів

	Байєсівське стекування ансамблів	Stacking
Галузь	Машинне навчання	Машинне навчання
Родина	Machine learning	Machine learning
Рік появи	2018	1992
Автор методу	Yao, Y.; Vehtari, A.; Simpson, D.; Gelman, A.	Wolpert, D.H.
Тип	Bayesian ensemble combination	Ensemble (heterogeneous meta-learning)
Основоположне джерело	Yao, Y., Vehtari, A., Simpson, D., & Gelman, A. (2018). Using stacking to average Bayesian predictive distributions. Bayesian Analysis, 13(3), 917–1007.	Wolpert, D.H. (1992). Stacked Generalization. Neural Networks, 5(2), 241–259.
Інші назви	Bayesian stacking, Bayesian model stacking, stacking with Bayesian weights, predictive distribution stacking	Stacking (Yığınlama — Meta-Öğrenme), stacked generalization, meta-learning ensemble, super learner
Пов'язані	6	5
Підсумок	Bayesian stacking combines the predictive distributions of several base models by finding non-negative weights that maximise the leave-one-out log predictive score of the mixture. Formalised by Yao, Vehtari, Simpson, and Gelman (2018), it yields a single calibrated predictive distribution that is provably at least as good as any single constituent model under cross-validation.	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.