مقایسهٔ روش‌ها

	رگرسیون لجستیک	ماشین بردار پشتیبان (طبقه‌بندی)
حوزه	آمار پژوهش	یادگیری ماشین
خانواده	Process / pipeline	Machine learning
سال پیدایش	1958	1995
پدیدآور	David Roxbee Cox	Cortes, C. & Vapnik, V.
نوع	Method	Maximum-margin classifier (kernel method)
منبع بنیادین	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.
نام‌های دیگر	logit model, binomial logistic regression, LR	Destek Vektör Makinesi (SVM — Sınıflandırma), support-vector network, SVM classifier, maximum-margin classifier
مرتبط	3	5
خلاصه	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.