مقایسهٔ روش‌ها

	بیزی نایو بیز (Bayesian Naive Bayes)	رگرسیون لجستیک (یادگیری ماشین)
حوزه	یادگیری ماشین	یادگیری ماشین
خانواده	Machine learning	Machine learning
سال پیدایش	1960s (base); Bayesian parameter treatment formalized 2000s	1958
پدیدآور	Naive Bayes: Maron & Kuhns (1960); full Bayesian treatment formalized by Murphy (2012) and Bishop (2006)	Cox, D. R.
نوع	Probabilistic generative classifier	Probabilistic linear classifier
منبع بنیادین	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.
نام‌های دیگر	Bayesian NB, Naive Bayes with Bayesian parameter estimation, Dirichlet-Multinomial Naive Bayes, BNB	logit model, logit regression, binomial logistic regression, maximum entropy classifier
مرتبط	4	5
خلاصه	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.