방법 비교

	베이즈 스태킹 앙상블	부스팅
분야	머신러닝	머신러닝
계열	Machine learning	Machine learning
기원 연도	2018	1990–1997
창시자	Yao, Y.; Vehtari, A.; Simpson, D.; Gelman, A.	Schapire, R. E.; Freund, Y.
유형	Bayesian ensemble combination	Sequential ensemble (iterative reweighting)
원전	Yao, Y., Vehtari, A., Simpson, D., & Gelman, A. (2018). Using stacking to average Bayesian predictive distributions. Bayesian Analysis, 13(3), 917–1007.	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.
별칭	Bayesian stacking, Bayesian model stacking, stacking with Bayesian weights, predictive distribution stacking	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble
관련	6	6
요약	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.	Boosting is a sequential ensemble technique that converts many simple, barely-better-than-chance learners into a single highly accurate model by repeatedly focusing training on the examples that previous learners got wrong, then combining all learners with weights proportional to their individual accuracy.