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| Bagging (Bootstrap Aggregating)× | Mitjana de models bayesians× | |
|---|---|---|
| Camp≠ | Aprenentatge automàtic | Bayesià |
| Família≠ | Machine learning | Bayesian methods |
| Any d'origen≠ | 1996 | 1999 |
| Autor original≠ | Breiman, L. | Hoeting, Madigan, Raftery & Volinsky |
| Tipus≠ | Ensemble meta-algorithm (variance reduction via bootstrap aggregation) | Bayesian model averaging |
| Font seminal≠ | Breiman, L. (1996). Bagging Predictors. Machine Learning, 24(2), 123–140. DOI ↗ | Hoeting, J. A., Madigan, D., Raftery, A. E. & Volinsky, C. T. (1999). Bayesian Model Averaging: A Tutorial. Statistical Science, 14(4), 382–401. link ↗ |
| Àlies≠ | Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor | BMA, Bayesian model combination, Bayesian Model Ortalaması (BMA) |
| Relacionats | 5 | 5 |
| Resum≠ | 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. |
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