Sammenlign metoder

	Online Boosting	Random Forest
Fagfelt	Maskinlæring	Maskinlæring
Familie	Machine learning	Machine learning
Opprinnelsesår	2001	2001
Opphavsperson	Oza, N. C. & Russell, S.	Breiman, L.
Type	Online ensemble (incremental boosting)	Ensemble (bagging of decision trees)
Opprinnelig kilde	Oza, N. C., & Russell, S. (2001). Online Bagging and Boosting. In Artificial Intelligence and Statistics 2001 (pp. 105–112). Morgan Kaufmann.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Alias	streaming boosting, incremental boosting, online AdaBoost, online ensemble boosting	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Relaterte	6	4
Sammendrag	Online Boosting adapts the classical boosting framework to data streams, updating an ensemble of weak learners one example at a time without storing the full dataset. The Oza-Russell formulation approximates AdaBoost's reweighting using Poisson-sampled instance counts, enabling accurate, adaptive classification in real-time or resource-constrained environments.	Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree.