Módszerek összehasonlítása

	Döntési fa	Logistic Regression	XGBoost
Tudományterület	Gépi tanulás	Kutatási statisztika	Gépi tanulás
Módszercsalád	Machine learning	Process / pipeline	Machine learning
Keletkezés éve	1984	1958	2016
Megalkotó	Breiman, Friedman, Olshen & Stone	David Roxbee Cox	Chen, T. & Guestrin, C.
Típus	Recursive partitioning (if-then rules)	Method	Ensemble (gradient-boosted decision trees)
Alapmű	Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth.	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.	Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794.
Alternatív nevek	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree	logit model, binomial logistic regression, LR	XGBoost, extreme gradient boosting, scalable tree boosting
Kapcsolódó	5	3	5
Összefoglaló	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.	Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science.	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.