Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Multinomial Logistic Regression× | Régression logistique× | |
|---|---|---|
| Domaine≠ | Statistique | Statistiques de recherche |
| Famille≠ | Regression model | Process / pipeline |
| Année d'origine≠ | 1966–1974 | 1958 |
| Auteur d'origine≠ | Cox (1966); Theil (1969); formalized by McFadden (1974) | David Roxbee Cox |
| Type≠ | Generalized linear model | Method |
| Source fondatrice≠ | Agresti, A. (2002). Categorical Data Analysis (2nd ed.). Wiley-Interscience. ISBN: 978-0471360933 | Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242. DOI ↗ |
| Alias≠ | polytomous logistic regression, softmax regression, multinomial logit, nominal logistic regression | logit model, binomial logistic regression, LR |
| Apparentées≠ | 4 | 3 |
| Résumé≠ | Multinomial logistic regression extends binary logistic regression to outcomes with three or more unordered categories. It models the log-odds of each category relative to a chosen reference category as a linear function of the predictors, and estimates all parameters simultaneously via maximum likelihood. It is the standard choice when the dependent variable is nominal with multiple levels. | 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. |
| ScholarGateJeu de données ↗ |
|
|