Compară metode

	Bagging Online	Bagging (Agregare Bootstrap)	Pădurea Aleatoare (Random Forest)
Domeniu	Învățare automată	Învățare automată	Învățare automată
Familie	Machine learning	Machine learning	Machine learning
Anul apariției	2001	1996	2001
Autorul original	Oza, N. C. & Russell, S.	Breiman, L.	Breiman, L.
Tip	Online ensemble (streaming bagging)	Ensemble meta-algorithm (variance reduction via bootstrap aggregation)	Ensemble (bagging of decision trees)
Sursa seminală	Oza, N. C., & Russell, S. (2001). Online bagging and boosting. In Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics (AISTATS 2001), pp. 105–112.	Breiman, L. (1996). Bagging Predictors. Machine Learning, 24(2), 123–140.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Denumiri alternative	incremental bagging, streaming bagging, online bootstrap aggregating, OzaBag	Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Înrudite	4	5	4
Rezumat	Online Bagging is a streaming ensemble method introduced by Oza and Russell in 2001 that adapts the classical bootstrap aggregating (Bagging) framework to the online learning setting. Instead of resampling a fixed dataset, each incoming instance is fed to every base learner a Poisson(1)-distributed number of times, faithfully approximating bootstrap sampling as the stream evolves. The result is a robust, incrementally updated ensemble that can handle concept drift and continuous data arrival without storing the entire dataset.	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.	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.