विधियों की तुलना करें

	लॉजिस्टिक रिग्रेशन	प्लैकेट-ल्यूस मॉडल
क्षेत्र	अनुसंधान सांख्यिकी	निर्णयन
परिवार	Process / pipeline	Regression model
उद्भव वर्ष	1958	1975
प्रवर्तक	David Roxbee Cox	Robin Plackett; R. Duncan Luce
प्रकार	Method	Probabilistic ranking model
मौलिक स्रोत	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.	Plackett, R. L. (1975). The analysis of permutations. Journal of the Royal Statistical Society: Series C, 24(2), 193–202.
उपनाम	logit model, binomial logistic regression, LR	Luce's Choice Axiom Model, Rank-Ordered Logit Model, Exploded Logit Model, Sıralama Tercih Modeli
संबंधित	3	3
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 Plackett-Luce model is a probabilistic framework for analysing and predicting rank-ordered data. Introduced by Robin Plackett (1975) — building on R. Duncan Luce's earlier axiom of choice (1959) — it models the probability of any complete ranking of items as a sequential selection process, where each item's chance of being chosen at each position is proportional to its latent worth parameter. It is widely used in preference learning, recommender systems, and choice modelling.