Comparar métodos

	Online Gradient Boosting	Boosting	Random Forest Online
Área	Aprendizado de máquina	Aprendizado de máquina	Aprendizado de máquina
Família	Machine learning	Machine learning	Machine learning
Ano de origem	2011–2015	1990–1997	2009
Autor original	Grubb, A. & Bagnell, J. A.; Beygelzimer, A. et al.	Schapire, R. E.; Freund, Y.	Saffari, A. et al.
Tipo	Online ensemble (sequential boosting on streaming data)	Sequential ensemble (iterative reweighting)	Incremental ensemble (streaming decision trees)
Fonte seminal	Grubb, A. & Bagnell, J. A. (2011). Generalized Boosting Algorithms for Convex Optimization. Proceedings of the 28th International Conference on Machine Learning (ICML 2011), 1209–1216.	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.
Outros nomes	OGB, streaming gradient boosting, incremental gradient boosting, online boosting with gradient descent	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble	ORF, streaming random forest, incremental random forest, adaptive random forest
Relacionados	6	6	6
Resumo	Online Gradient Boosting adapts the gradient boosting framework for streaming settings where data arrives one sample at a time rather than as a fixed batch. At each step the model computes a pseudo-residual for the incoming observation and updates a weak learner in place, growing an additive ensemble without storing or revisiting past data. This makes it suitable for real-time prediction and large-scale streaming pipelines where retraining from scratch is infeasible.	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.