Uporedite metode

	XGBoost	Градијентно појачање	Logistička regresija
Oblast	Mašinsko učenje	Mašinsko učenje	Istraživačka statistika
Porodica	Machine learning	Machine learning	Process / pipeline
Godina nastanka	2016	2001	1958
Tvorac	Chen, T. & Guestrin, C.	Friedman, J. H.	David Roxbee Cox
Tip	Ensemble (gradient-boosted decision trees)	Ensemble (sequential boosting of decision trees)	Method
Temeljni izvor	Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794.	Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232.	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.
Drugi nazivi	XGBoost, extreme gradient boosting, scalable tree boosting	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine	logit model, binomial logistic regression, LR
Srodne	5	5	3
Sažetak	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.	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.	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.