Comparer des méthodes

	Gradient Boosting en ligne	XGBoost
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2011–2015	2016
Auteur d'origine	Grubb, A. & Bagnell, J. A.; Beygelzimer, A. et al.	Chen, T. & Guestrin, C.
Type	Online ensemble (sequential boosting on streaming data)	Ensemble (gradient-boosted decision trees)
Source fondatrice	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.
Alias	OGB, streaming gradient boosting, incremental gradient boosting, online boosting with gradient descent	XGBoost, extreme gradient boosting, scalable tree boosting
Apparentées	6	5
Résumé	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.