Methoden vergleichen

	Gradient Boosting	Online Gradient Boosting
Fachgebiet	Maschinelles Lernen	Maschinelles Lernen
Familie	Machine learning	Machine learning
Entstehungsjahr	2001	2011–2015
Urheber	Friedman, J. H.	Grubb, A. & Bagnell, J. A.; Beygelzimer, A. et al.
Typ	Ensemble (sequential boosting of decision trees)	Online ensemble (sequential boosting on streaming data)
Wegweisende Quelle	Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232.	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.
Aliasnamen	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine	OGB, streaming gradient boosting, incremental gradient boosting, online boosting with gradient descent
Verwandt	5	6
Zusammenfassung	Gradient Boosting is an ensemble learning method, formalised by Jerome H. Friedman in 2001, that combines a sequence of weak learners — typically shallow decision trees — so that each new tree is fitted to minimise the residual errors of the trees before it. It is the core algorithm behind popular implementations such as XGBoost, LightGBM and CatBoost.	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.