방법 비교

	부스팅	온라인 랜덤 포레스트
분야	머신러닝	머신러닝
계열	Machine learning	Machine learning
기원 연도	1990–1997	2009
창시자	Schapire, R. E.; Freund, Y.	Saffari, A. et al.
유형	Sequential ensemble (iterative reweighting)	Incremental ensemble (streaming decision trees)
원전	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.	Saffari, A., Leistner, C., Santner, J., Godec, M., & Bischof, H. (2009). On-line random forests. In Proceedings of the 3rd IEEE International Workshop on On-Line Learning for Computer Vision (OLCV 2009), pp. 1–8. IEEE.
별칭	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble	ORF, streaming random forest, incremental random forest, adaptive random forest
관련	6	6
요약	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.	Online Random Forest (ORF) extends the classic Random Forest to streaming settings, updating each tree incrementally as new observations arrive without storing or replaying the full training set. Algorithms such as Adaptive Random Forests (ARF) add drift detection so the ensemble adapts when the data distribution changes over time.