Comparar métodos

	Online Gradient Boosting	XGBoost
Área	Aprendizado de máquina	Aprendizado de máquina
Família	Machine learning	Machine learning
Ano de origem	2011–2015	2016
Autor original	Grubb, A. & Bagnell, J. A.; Beygelzimer, A. et al.	Chen, T. & Guestrin, C.
Tipo	Online ensemble (sequential boosting on streaming data)	Ensemble (gradient-boosted 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.	Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794.
Outros nomes	OGB, streaming gradient boosting, incremental gradient boosting, online boosting with gradient descent	XGBoost, extreme gradient boosting, scalable tree boosting
Relacionados	6	5
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.	XGBoost (Extreme Gradient Boosting) is a scalable tree-boosting algorithm introduced by Tianqi Chen and Carlos Guestrin in 2016. It builds a strong predictor by adding decision trees one at a time, each correcting the errors left by the trees before it, and is a powerful prediction method widely used in competitions.