Sammenlign metoder

	Ensemble Gradient Boosting	AdaBoost	CatBoost
Fagfelt	Maskinlæring	Maskinlæring	Maskinlæring
Familie	Machine learning	Machine learning	Machine learning
Opprinnelsesår	2001	1997	2018
Opphavsperson	Friedman, J. H.	Freund, Y. & Schapire, R.E.	Prokhorenkova, L. et al. (Yandex)
Type	Ensemble (sequential boosting of decision trees)	Ensemble (sequential boosting of weak learners)	Gradient boosting on decision trees
Opprinnelig kilde	Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189–1232.	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.	Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A.V. & Gulin, A. (2018). CatBoost: Unbiased Boosting with Categorical Features. In NeurIPS 2018.
Alias	Gradient Boosting Machine, GBM, Gradient Tree Boosting, Stochastic Gradient Boosting	AdaBoost (Adaptive Boosting), adaptive boosting, adaptif artırma	CatBoost (Categorical Boosting), categorical boosting, ordered boosting, kategorik gradyan artırma
Relaterte	6	5	5
Sammendrag	Gradient Boosting is an ensemble method introduced by Jerome Friedman in 2001 that builds a strong predictive model by sequentially adding shallow decision trees, each correcting the errors of the previous ensemble. By framing the problem as gradient descent in function space, it achieves state-of-the-art accuracy on classification, regression, and ranking tasks across tabular data.	AdaBoost (Adaptive Boosting) is the original boosting algorithm, introduced by Yoav Freund and Robert Schapire in 1997, that combines a sequence of simple weak learners by giving more weight to the observations they get wrong. The forerunner of gradient boosting, it is simple, interpretable, and a strong baseline for classification.	CatBoost is a gradient boosting algorithm, introduced by Prokhorenkova and colleagues at Yandex in 2018, that handles categorical variables natively and uses ordered target encoding to avoid label leakage. By building an additive ensemble of trees while permuting the data order at each iteration, it is often superior to XGBoost and LightGBM on category-heavy data.