手法を比較

	説明可能なナイーブベイズ	ランダムフォレスト
分野	機械学習	機械学習
系統	Machine learning	Machine learning
提唱年	1950s (Naive Bayes); 2000s–2010s (explainability focus)	2001
提唱者	Zhang, H. (explainability framing); Naive Bayes: Good, I. J.	Breiman, L.
種類	Probabilistic generative classifier with intrinsic explainability	Ensemble (bagging of decision trees)
原典	Rish, I. (2001). An empirical study of the naive Bayes classifier. In IJCAI Workshop on Empirical Methods in AI (pp. 41–46).	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
別名	XNB, interpretable Naive Bayes, transparent Naive Bayes, explainable probabilistic classifier	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
関連	4	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.	Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree.