Porovnať metódy

	Boosting	Rozhodovací strom	Gradient Boosting
Odbor	Strojové učenie	Strojové učenie	Strojové učenie
Rodina	Machine learning	Machine learning	Machine learning
Rok vzniku	1990–1997	1984	2001
Tvorca	Schapire, R. E.; Freund, Y.	Breiman, Friedman, Olshen & Stone	Friedman, J. H.
Typ	Sequential ensemble (iterative reweighting)	Recursive partitioning (if-then rules)	Ensemble (sequential boosting of decision trees)
Pôvodný zdroj	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.	Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth.	Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232.
Ďalšie názvy	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine
Príbuzné	6	5	5
Zhrnutie	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.	A Decision Tree is an interpretable classification and regression method, formalised by Breiman, Friedman, Olshen and Stone in their 1984 CART framework, that partitions the data with hierarchical if-then rules. Each split sends observations down one branch or another until a prediction is read off the leaf.	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.