Krahasoni metodat

	Pemët e vendimmarrjes	XGBoost
Fusha	Mësimi i makinës	Mësimi i makinës
Familja	Machine learning	Machine learning
Viti i origjinës	1984	2016
Krijuesi	Breiman, Friedman, Olshen & Stone	Chen, T. & Guestrin, C.
Lloji	Recursive partitioning (if-then rules)	Ensemble (gradient-boosted decision trees)
Burimi themelues	Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth.	Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794.
Emërtime të tjera	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree	XGBoost, extreme gradient boosting, scalable tree boosting
Të lidhura	5	5
Përmbledhja	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.	XGBoost (Extreme Gradient Boosting) is a scalable tree-boosting algorithm introduced by Tianqi Chen and Carlos Guestrin in 2016. It builds a strong predictor by adding decision trees one at a time, each correcting the errors left by the trees before it, and is a powerful prediction method widely used in competitions.