Compara mètodes

	Naive Bayes Robusta	Regressió Logística
Camp	Aprenentatge automàtic	Estadística per a la recerca
Família	Machine learning	Process / pipeline
Any d'origen	2002	1958
Autor original	Zaffalon, M.	David Roxbee Cox
Tipus	Probabilistic generative classifier with imprecise-probability robustness	Method
Font seminal	Zaffalon, M. (2002). The Naive Credal Classifier. Journal of Statistical Planning and Inference, 105(1), 5–21.	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.
Àlies	Naive Credal Classifier, NCC, Robust Bayesian Naive Classifier, Imprecise Naive Bayes	logit model, binomial logistic regression, LR
Relacionats	3	3
Resum	Robust Naive Bayes extends the standard Naive Bayes classifier to handle uncertainty or noise in class-conditional probability estimates by replacing point probability estimates with intervals or sets of distributions. The canonical formulation — the Naive Credal Classifier proposed by Zaffalon (2002) — uses imprecise-probability sets so that predictions are made only when all distributions in the set agree, withholding a label when evidence is insufficient.	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.