পদ্ধতির তুলনা করুন

	ব্যাখ্যাযোগ্য সাপোর্ট ভেক্টর মেশিন	ব্যাখ্যাযোগ্য বেইজ (Explainable Naive Bayes)
ক্ষেত্র	যন্ত্র শিখন	যন্ত্র শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	2016–2017 (XAI layer)	1950s (Naive Bayes); 2000s–2010s (explainability focus)
প্রবর্তক	Cortes & Vapnik (SVM); explainability layer via Lundberg & Lee (SHAP, 2017) and Ribeiro et al. (LIME, 2016)	Zhang, H. (explainability framing); Naive Bayes: Good, I. J.
ধরন	Post-hoc explainability applied to SVM	Probabilistic generative classifier with intrinsic explainability
মৌলিক উৎস	Lundberg, S. M., & Lee, S. I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 4765–4774.	Rish, I. (2001). An empirical study of the naive Bayes classifier. In IJCAI Workshop on Empirical Methods in AI (pp. 41–46).
অপর নাম	Explainable SVM, Interpretable SVM, XAI-SVM, Transparent Support Vector Machine	XNB, interpretable Naive Bayes, transparent Naive Bayes, explainable probabilistic classifier
সম্পর্কিত	4	4
সারসংক্ষেপ	Explainable SVM combines a trained Support Vector Machine with a post-hoc interpretability layer — typically SHAP or LIME — to produce feature-level explanations for individual predictions and global importance rankings. It retains the discriminative power of SVM while meeting transparency requirements in high-stakes domains such as medicine, finance, and law.	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.