Comparar métodos

	Online Bagging	Agregación por Bootstrap (Bagging)	Impulso en línea	Random Forest
Campo	Aprendizaje automático	Aprendizaje automático	Aprendizaje automático	Aprendizaje automático
Familia	Machine learning	Machine learning	Machine learning	Machine learning
Año de origen	2001	1996	2001	2001
Autor original	Oza, N. C. & Russell, S.	Breiman, L.	Oza, N. C. & Russell, S.	Breiman, L.
Tipo	Online ensemble (streaming bagging)	Ensemble meta-algorithm (variance reduction via bootstrap aggregation)	Online ensemble (incremental boosting)	Ensemble (bagging of decision trees)
Fuente seminal	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.	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	incremental bagging, streaming bagging, online bootstrap aggregating, OzaBag	Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor	streaming boosting, incremental boosting, online AdaBoost, online ensemble boosting	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Relacionados	4	5	6	4
Resumen	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.	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.