مقایسهٔ روش‌ها

	رگرسیون لجستیک	چیدمان
حوزه	آمار پژوهش	یادگیری ماشین
خانواده	Process / pipeline	Machine learning
سال پیدایش	1958	1992
پدیدآور	David Roxbee Cox	Wolpert, D.H.
نوع	Method	Ensemble (heterogeneous meta-learning)
منبع بنیادین	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.	Wolpert, D.H. (1992). Stacked Generalization. Neural Networks, 5(2), 241–259.
نام‌های دیگر	logit model, binomial logistic regression, LR	Stacking (Yığınlama — Meta-Öğrenme), stacked generalization, meta-learning ensemble, super learner
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.	Stacking, or stacked generalization, is an ensemble method introduced by David Wolpert in 1992 that combines the outputs of several different base models (Level-0) through a separate meta-model (Level-1). Unlike bagging and boosting, it deliberately uses heterogeneous model types, and it is the standard final-stage strategy in Kaggle competitions.