Compara mètodes

	Naive Bayes Explicable	Regressió Logística
Camp	Aprenentatge automàtic	Estadística per a la recerca
Família	Machine learning	Process / pipeline
Any d'origen	1950s (Naive Bayes); 2000s–2010s (explainability focus)	1958
Autor original	Zhang, H. (explainability framing); Naive Bayes: Good, I. J.	David Roxbee Cox
Tipus	Probabilistic generative classifier with intrinsic explainability	Method
Font seminal	Rish, I. (2001). An empirical study of the naive Bayes classifier. In IJCAI Workshop on Empirical Methods in AI (pp. 41–46).	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.
Àlies	XNB, interpretable Naive Bayes, transparent Naive Bayes, explainable probabilistic classifier	logit model, binomial logistic regression, LR
Relacionats	4	3
Resum	Explainable Naive Bayes extends the classic probabilistic Naive Bayes classifier with transparent, human-readable explanations of its predictions. By surfacing class priors, per-feature likelihoods, and log-odds contributions, it offers the interpretability demanded in high-stakes domains such as medicine, law, and education without sacrificing the simplicity and speed that make Naive Bayes a reliable baseline.	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.