Linganisha mbinu

	Dharira ya Msaidizi inayoeleweka	Bayesi ya Ufafanuzi
Nyanja	Ujifunzaji wa Mashine	Ujifunzaji wa Mashine
Familia	Machine learning	Machine learning
Mwaka wa asili	2016–2017 (XAI layer)	1950s (Naive Bayes); 2000s–2010s (explainability focus)
Mwanzilishi	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.
Aina	Post-hoc explainability applied to SVM	Probabilistic generative classifier with intrinsic explainability
Chanzo asilia	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).
Majina mbadala	Explainable SVM, Interpretable SVM, XAI-SVM, Transparent Support Vector Machine	XNB, interpretable Naive Bayes, transparent Naive Bayes, explainable probabilistic classifier
Zinazohusiana	4	4
Muhtasari	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.