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| Bayesian Stacking Ensemble× | Bagging (Bootstrap Aggregating)× | |
|---|---|---|
| Ämnesområde | Maskininlärning | Maskininlärning |
| Familj | Machine learning | Machine learning |
| Ursprungsår≠ | 2018 | 1996 |
| Upphovsperson≠ | Yao, Y.; Vehtari, A.; Simpson, D.; Gelman, A. | Breiman, L. |
| Typ≠ | Bayesian ensemble combination | Ensemble meta-algorithm (variance reduction via bootstrap aggregation) |
| Ursprungskälla≠ | Yao, Y., Vehtari, A., Simpson, D., & Gelman, A. (2018). Using stacking to average Bayesian predictive distributions. Bayesian Analysis, 13(3), 917–1007. DOI ↗ | Breiman, L. (1996). Bagging Predictors. Machine Learning, 24(2), 123–140. DOI ↗ |
| Alias≠ | Bayesian stacking, Bayesian model stacking, stacking with Bayesian weights, predictive distribution stacking | Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor |
| Närliggande≠ | 6 | 5 |
| Sammanfattning≠ | 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. |
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