Porovnat metody

	Bayesian Stacking Ensemble	Bagging (Bootstrap Aggregating)
Obor	Strojové učení	Strojové učení
Rodina	Machine learning	Machine learning
Rok vzniku	2018	1996
Tvůrce	Yao, Y.; Vehtari, A.; Simpson, D.; Gelman, A.	Breiman, L.
Typ	Bayesian ensemble combination	Ensemble meta-algorithm (variance reduction via bootstrap aggregation)
Původní zdroj	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.
Další názvy	Bayesian stacking, Bayesian model stacking, stacking with Bayesian weights, predictive distribution stacking	Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor
Příbuzné	6	5
Shrnutí	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.