Sammenlign metoder

	Gradient Boosting	Online læring
Fagområde	Maskinlæring	Maskinlæring
Familie	Machine learning	Machine learning
Oprindelsesår	2001	1958–2000s
Ophavsperson	Friedman, J. H.	Rosenblatt, F.; Littlestone, N.; Shalev-Shwartz, S. (key contributors)
Type	Ensemble (sequential boosting of decision trees)	Learning paradigm (sequential model update)
Oprindelig kilde	Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232.	Shalev-Shwartz, S. (2011). Online Learning and Online Convex Optimization. Foundations and Trends in Machine Learning, 4(2), 107–194.
Aliasser	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine	incremental learning, sequential learning, streaming learning, online machine learning
Relaterede	5	6
Resumé	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 learning is a machine learning paradigm in which a model is updated incrementally as each new data point arrives, rather than being trained once on a fixed dataset. It is essential when data streams continuously, storage is limited, or the underlying distribution shifts over time. Theoretical performance is measured by cumulative regret relative to the best fixed predictor in hindsight.