Salīdzināt metodes

	Skaidrojams Naive Bayes	Koku lēmumu pieņemšana (Decision Tree)
Nozare	Mašīnmācīšanās	Mašīnmācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	1950s (Naive Bayes); 2000s–2010s (explainability focus)	1984
Autors	Zhang, H. (explainability framing); Naive Bayes: Good, I. J.	Breiman, Friedman, Olshen & Stone
Tips	Probabilistic generative classifier with intrinsic explainability	Recursive partitioning (if-then rules)
Pirmavots	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.
Citi nosaukumi	XNB, interpretable Naive Bayes, transparent Naive Bayes, explainable probabilistic classifier	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree
Saistītās	4	5
Kopsavilkums	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.