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	Boosting Ensemble	Gradient Boosting
Dziedzina	Uczenie zespołowe	Uczenie maszynowe
Rodzina	Machine learning	Machine learning
Rok powstania	1990	2001
Twórca	Robert Schapire	Friedman, J. H.
Typ	sequential ensemble	Ensemble (sequential boosting of decision trees)
Źródło pierwotne	Schapire, R. E. (1990). The strength of weak learnability. Machine Learning, 5(2), 197-227.	Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232.
Inne nazwy	adaptive boosting, sequential ensemble	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine
Pokrewne	4	5
Podsumowanie	Boosting is an ensemble method that sequentially trains weak learners and combines them into a strong predictor by focusing on samples that previous models misclassified. Each new weak learner is weighted according to the difficulty of its training task, and final predictions are made via weighted voting. Pioneered by Schapire (1990) and refined in AdaBoost (Freund & Schapire, 1997), boosting converts weak learners (barely better than random) into strong learners through sequential reweighting.	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.