Comparer des méthodes

	Classificateur Naive Bayes bayésien	Régression logistique (ML)
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	1960s (base); Bayesian parameter treatment formalized 2000s	1958
Auteur d'origine	Naive Bayes: Maron & Kuhns (1960); full Bayesian treatment formalized by Murphy (2012) and Bishop (2006)	Cox, D. R.
Type	Probabilistic generative classifier	Probabilistic linear classifier
Source fondatrice	Murphy, K. P. (2012). Machine Learning: A Probabilistic Perspective (Ch. 3, 4). MIT Press. ISBN: 978-0-262-01802-9	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.
Alias	Bayesian NB, Naive Bayes with Bayesian parameter estimation, Dirichlet-Multinomial Naive Bayes, BNB	logit model, logit regression, binomial logistic regression, maximum entropy classifier
Apparentées	4	5
Résumé	Bayesian Naive Bayes applies a fully Bayesian treatment to the parameters of the classic Naive Bayes classifier: instead of estimating class-conditional distributions by maximum likelihood, it places conjugate priors (typically Dirichlet for categorical data or Gaussian-Gamma for continuous data) over the parameters and integrates them out, producing predictive posterior distributions that naturally quantify uncertainty and avoid overfitting on small datasets.	Logistic regression is a foundational probabilistic classifier that models the log-odds of a binary (or multinomial) outcome as a linear function of the predictors. Introduced by D. R. Cox in 1958, it remains one of the most widely used and interpretable classification methods in both statistics and machine learning, valued for its calibrated probability outputs and clear coefficient interpretation.