পদ্ধতির তুলনা করুন

	ব্যাখ্যাযোগ্য সহযোগী নিয়ম (Explainable Association Rules)	ব্যাখ্যাযোগ্য বেইজ (Explainable Naive Bayes)
ক্ষেত্র	যন্ত্র শিখন	যন্ত্র শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	1993 (rules); 2010s (XAI framing)	1950s (Naive Bayes); 2000s–2010s (explainability focus)
প্রবর্তক	Agrawal, R., Imielinski, T., & Swami, A. (foundational); XAI framing: broader community (2010s–present)	Zhang, H. (explainability framing); Naive Bayes: Good, I. J.
ধরন	Interpretable pattern mining / XAI technique	Probabilistic generative classifier with intrinsic explainability
মৌলিক উৎস	Agrawal, R., Imielinski, T., & Swami, A. (1993). Mining association rules between sets of items in large databases. Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, 207–216.	Rish, I. (2001). An empirical study of the naive Bayes classifier. In IJCAI Workshop on Empirical Methods in AI (pp. 41–46).
অপর নাম	XAI association rules, interpretable association rules, rule-based explanation mining, transparent association rule learning	XNB, interpretable Naive Bayes, transparent Naive Bayes, explainable probabilistic classifier
সম্পর্কিত	6	4
সারসংক্ষেপ	Explainable Association Rules leverages the inherently symbolic, if-then structure of association rule mining to provide human-readable explanations of data patterns or black-box model decisions. Because each rule explicitly states its antecedent and consequent together with support, confidence, and lift, the outputs are natively interpretable without requiring a secondary post-hoc surrogate.	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.