Salīdzināt metodes

	Koku lēmumu pieņemšana (Decision Tree)	XGBoost
Nozare	Mašīnmācīšanās	Mašīnmācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	1984	2016
Autors	Breiman, Friedman, Olshen & Stone	Chen, T. & Guestrin, C.
Tips	Recursive partitioning (if-then rules)	Ensemble (gradient-boosted decision trees)
Pirmavots	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.
Citi nosaukumi	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree	XGBoost, extreme gradient boosting, scalable tree boosting
Saistītās	5	5
Kopsavilkums	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.