Methoden vergleichen

	XGBoost	Gradient Boosting	Logistische Regression
Fachgebiet	Maschinelles Lernen	Maschinelles Lernen	Forschungsstatistik
Familie	Machine learning	Machine learning	Process / pipeline
Entstehungsjahr	2016	2001	1958
Urheber	Chen, T. & Guestrin, C.	Friedman, J. H.	David Roxbee Cox
Typ	Ensemble (gradient-boosted decision trees)	Ensemble (sequential boosting of decision trees)	Method
Wegweisende Quelle	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.
Aliasnamen	XGBoost, extreme gradient boosting, scalable tree boosting	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine	logit model, binomial logistic regression, LR
Verwandt	5	5	3
Zusammenfassung	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.