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| Bayesian Boosting× | Boosting× | Gradient Boosting× | |
|---|---|---|---|
| Ämnesområde | Maskininlärning | Maskininlärning | Maskininlärning |
| Familj | Machine learning | Machine learning | Machine learning |
| Ursprungsår≠ | 1999–2010 | 1990–1997 | 2001 |
| Upphovsperson≠ | Ridgeway, G.; Chipman, H. A. et al. | Schapire, R. E.; Freund, Y. | Friedman, J. H. |
| Typ≠ | Probabilistic ensemble (Bayesian interpretation of boosting) | Sequential ensemble (iterative reweighting) | Ensemble (sequential boosting of decision trees) |
| Ursprungskälla≠ | Ridgeway, G. (1999). The state of boosting. Computing Science and Statistics, 31, 172–181. link ↗ | 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. DOI ↗ | Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232. DOI ↗ |
| Alias | Bayesian ensemble boosting, probabilistic boosting, Bayesian additive model, Bayesian boosted ensemble | AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble | Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine |
| Närliggande≠ | 5 | 6 | 5 |
| Sammanfattning≠ | Bayesian boosting integrates probabilistic Bayesian inference with boosting ensemble techniques, combining multiple weak learners while maintaining full uncertainty quantification over predictions. Unlike standard gradient boosting that produces a single point estimate, Bayesian boosting yields a posterior distribution over the ensemble output, enabling calibrated confidence intervals alongside predictions. | 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. | Gradient Boosting is an ensemble learning method, formalised by Jerome H. Friedman in 2001, that combines a sequence of weak learners — typically shallow decision trees — so that each new tree is fitted to minimise the residual errors of the trees before it. It is the core algorithm behind popular implementations such as XGBoost, LightGBM and CatBoost. |
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