विधियों की तुलना करें

	लॉजिस्टिक रिग्रेशन	सपोर्ट वेक्टर मशीन (वर्गीकरण)	XGBoost
क्षेत्र	अनुसंधान सांख्यिकी	मशीन अधिगम	मशीन अधिगम
परिवार	Process / pipeline	Machine learning	Machine learning
उद्भव वर्ष	1958	1995	2016
प्रवर्तक	David Roxbee Cox	Cortes, C. & Vapnik, V.	Chen, T. & Guestrin, C.
प्रकार	Method	Maximum-margin classifier (kernel method)	Ensemble (gradient-boosted decision trees)
मौलिक स्रोत	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.	Cortes, C. & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20, 273–297.	Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794.
उपनाम	logit model, binomial logistic regression, LR	Destek Vektör Makinesi (SVM — Sınıflandırma), support-vector network, SVM classifier, maximum-margin classifier	XGBoost, extreme gradient boosting, scalable tree boosting
संबंधित	3	5	5
सारांश	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.	The Support Vector Machine, introduced by Corinna Cortes and Vladimir Vapnik in 1995, is a classifier that finds the optimal separating hyperplane between classes in a high-dimensional space. It chooses the boundary that leaves the widest possible margin to the nearest training points, which makes its decisions robust on new data.	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.