Compară metode

	Naive Bayes explicabil	Arbore de decizie
Domeniu	Învățare automată	Învățare automată
Familie	Machine learning	Machine learning
Anul apariției	1950s (Naive Bayes); 2000s–2010s (explainability focus)	1984
Autorul original	Zhang, H. (explainability framing); Naive Bayes: Good, I. J.	Breiman, Friedman, Olshen & Stone
Tip	Probabilistic generative classifier with intrinsic explainability	Recursive partitioning (if-then rules)
Sursa seminală	Rish, I. (2001). An empirical study of the naive Bayes classifier. In IJCAI Workshop on Empirical Methods in AI (pp. 41–46).	Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth.
Denumiri alternative	XNB, interpretable Naive Bayes, transparent Naive Bayes, explainable probabilistic classifier	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree
Înrudite	4	5
Rezumat	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.	A Decision Tree is an interpretable classification and regression method, formalised by Breiman, Friedman, Olshen and Stone in their 1984 CART framework, that partitions the data with hierarchical if-then rules. Each split sends observations down one branch or another until a prediction is read off the leaf.