手法を比較

	説明可能なナイーブベイズ	ロジスティック回帰	ナイーブベイズ
分野	機械学習	研究統計	機械学習
系統	Machine learning	Process / pipeline	Machine learning
提唱年	1950s (Naive Bayes); 2000s–2010s (explainability focus)	1958	1997
提唱者	Zhang, H. (explainability framing); Naive Bayes: Good, I. J.	David Roxbee Cox	Mitchell, T. M. (textbook treatment)
種類	Probabilistic generative classifier with intrinsic explainability	Method	Probabilistic classifier (Bayes' theorem with conditional independence)
原典	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.	Mitchell, T. M. (1997). Machine Learning. McGraw-Hill. ISBN: 978-0070428072
別名	XNB, interpretable Naive Bayes, transparent Naive Bayes, explainable probabilistic classifier	logit model, binomial logistic regression, LR	Naive Bayes Sınıflandırıcı, naive bayes classifier, simple Bayes, Gaussian Naive Bayes
関連	4	3	4
概要	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.	Naive Bayes is a fast probabilistic classifier that applies Bayes' theorem while assuming that the features are conditionally independent given the class — a method given its standard machine-learning treatment in Tom Mitchell's 1997 textbook Machine Learning. Despite this simplifying ('naive') assumption, it is quick to train and often surprisingly accurate.