Võrdle meetodeid

	XGBoost	Logistiline regressioon
Valdkond	Masinõpe	Uurimisstatistika
Perekond	Machine learning	Process / pipeline
Tekkeaasta	2016	1958
Looja	Chen, T. & Guestrin, C.	David Roxbee Cox
Tüüp	Ensemble (gradient-boosted decision trees)	Method
Algallikas	Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794.	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.
Rööpnimetused	XGBoost, extreme gradient boosting, scalable tree boosting	logit model, binomial logistic regression, LR
Seotud	5	3
Kokkuvõte	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.	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.