পদ্ধতির তুলনা করুন

	ব্যাখ্যাযোগ্য বেইজ (Explainable Naive Bayes)	সিদ্ধান্ত বৃক্ষ	Random Forest
ক্ষেত্র	যন্ত্র শিখন	যন্ত্র শিখন	যন্ত্র শিখন
পরিবার	Machine learning	Machine learning	Machine learning
উদ্ভবের বছর	1950s (Naive Bayes); 2000s–2010s (explainability focus)	1984	2001
প্রবর্তক	Zhang, H. (explainability framing); Naive Bayes: Good, I. J.	Breiman, Friedman, Olshen & Stone	Breiman, L.
ধরন	Probabilistic generative classifier with intrinsic explainability	Recursive partitioning (if-then rules)	Ensemble (bagging of decision trees)
মৌলিক উৎস	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.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
অপর নাম	XNB, interpretable Naive Bayes, transparent Naive Bayes, explainable probabilistic classifier	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
সম্পর্কিত	4	5	4
সারসংক্ষেপ	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.	Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree.