방법 비교

	베이즈 스태킹 앙상블	배깅 (Bootstrap Aggregating)	Bayesian Model Averaging	부스팅
분야	머신러닝	머신러닝	베이지안	머신러닝
계열	Machine learning	Machine learning	Bayesian methods	Machine learning
기원 연도	2018	1996	1999	1990–1997
창시자	Yao, Y.; Vehtari, A.; Simpson, D.; Gelman, A.	Breiman, L.	Hoeting, Madigan, Raftery & Volinsky	Schapire, R. E.; Freund, Y.
유형	Bayesian ensemble combination	Ensemble meta-algorithm (variance reduction via bootstrap aggregation)	Bayesian model averaging	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.	Breiman, L. (1996). Bagging Predictors. Machine Learning, 24(2), 123–140.	Hoeting, J. A., Madigan, D., Raftery, A. E. & Volinsky, C. T. (1999). Bayesian Model Averaging: A Tutorial. Statistical Science, 14(4), 382–401.	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	Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor	BMA, Bayesian model combination, Bayesian Model Ortalaması (BMA)	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble
관련	6	5	5	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.	Bagging, short for Bootstrap Aggregating, is an ensemble meta-algorithm introduced by Leo Breiman in 1996 that trains multiple copies of a base learner on independently drawn bootstrap samples of the training data and combines their predictions — by averaging for regression or majority vote for classification — to produce a final predictor with substantially lower variance than any single base learner.	Bayesian Model Averaging (BMA), formalised as a tutorial by Hoeting, Madigan, Raftery and Volinsky in 1999, addresses model uncertainty by averaging over all plausible model specifications rather than selecting a single best model. Each candidate model receives a posterior probability that reflects how well it fits the data given a prior, and predictions or coefficient estimates are formed as weighted averages across the entire model space. This approach reduces the bias and overconfidence that arise when a single selected model is treated as the true one.	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.