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| 베이즈 랜덤 포레스트× | 그래디언트 부스팅× | |
|---|---|---|
| 분야 | 머신러닝 | 머신러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2015 | 2001 |
| 창시자≠ | Taddy, M. et al. | Friedman, J. H. |
| 유형≠ | Bayesian ensemble of decision trees | Ensemble (sequential boosting of decision trees) |
| 원전≠ | Taddy, M., Chen, C., Yu, J., & Wyle, M. (2015). Bayesian and Empirical Bayesian Forests. Proceedings of the 32nd International Conference on Machine Learning (ICML 2015), PMLR 37, 967–976. link ↗ | Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232. DOI ↗ |
| 별칭 | Bayesian Forest, BRF, Empirical Bayesian Forest, posterior random forest | Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine |
| 관련 | 5 | 5 |
| 요약≠ | Bayesian Random Forest extends the classical random forest by placing a prior distribution over tree structures and leaf parameters, then sampling or approximating the posterior over that ensemble. The result is a set of predictions accompanied by calibrated uncertainty estimates — a capability standard random forests lack — making it valuable when knowing how confident the model is matters as much as the prediction itself. | 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. |
| ScholarGate데이터셋 ↗ |
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