Sammenlign metoder

	Boosting	Gradient Boosting	Regulert beslutningstre
Fagfelt	Maskinlæring	Maskinlæring	Maskinlæring
Familie	Machine learning	Machine learning	Machine learning
Opprinnelsesår	1990–1997	2001	1984
Opphavsperson	Schapire, R. E.; Freund, Y.	Friedman, J. H.	Breiman, L., Friedman, J., Olshen, R., & Stone, C.
Type	Sequential ensemble (iterative reweighting)	Ensemble (sequential boosting of decision trees)	Supervised learning (regularized tree)
Opprinnelig kilde	Freund, Y. & Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1), 119–139.	Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232.	Breiman, L., Friedman, J., Olshen, R., & Stone, C. (1984). Classification and Regression Trees. Wadsworth. ISBN: 978-0-412-04841-8
Alias	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine	pruned decision tree, cost-complexity pruned tree, penalized decision tree, constrained CART
Relaterte	6	5	6
Sammendrag	Boosting is a sequential ensemble technique that converts many simple, barely-better-than-chance learners into a single highly accurate model by repeatedly focusing training on the examples that previous learners got wrong, then combining all learners with weights proportional to their individual accuracy.	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.	A regularized decision tree is a decision tree model whose complexity is intentionally limited through pruning, depth constraints, or penalty terms to prevent overfitting. Rooted in Breiman et al.'s CART framework (1984), regularization converts the greedy tree-growing procedure into a bias-variance tradeoff, yielding models that generalize better to unseen data than fully-grown trees.