Linganisha mbinu

	Bayesi ya Ufafanuzi	Mti wa Uamuzi	Regresheni ya Logistiki	Naive Bayes
Nyanja	Ujifunzaji wa Mashine	Ujifunzaji wa Mashine	Takwimu za Utafiti	Ujifunzaji wa Mashine
Familia	Machine learning	Machine learning	Process / pipeline	Machine learning
Mwaka wa asili	1950s (Naive Bayes); 2000s–2010s (explainability focus)	1984	1958	1997
Mwanzilishi	Zhang, H. (explainability framing); Naive Bayes: Good, I. J.	Breiman, Friedman, Olshen & Stone	David Roxbee Cox	Mitchell, T. M. (textbook treatment)
Aina	Probabilistic generative classifier with intrinsic explainability	Recursive partitioning (if-then rules)	Method	Probabilistic classifier (Bayes' theorem with conditional independence)
Chanzo asilia	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.	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.	Mitchell, T. M. (1997). Machine Learning. McGraw-Hill. ISBN: 978-0070428072
Majina mbadala	XNB, interpretable Naive Bayes, transparent Naive Bayes, explainable probabilistic classifier	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree	logit model, binomial logistic regression, LR	Naive Bayes Sınıflandırıcı, naive bayes classifier, simple Bayes, Gaussian Naive Bayes
Zinazohusiana	4	5	3	4
Muhtasari	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.	Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science.	Naive Bayes is a fast probabilistic classifier that applies Bayes' theorem while assuming that the features are conditionally independent given the class — a method given its standard machine-learning treatment in Tom Mitchell's 1997 textbook Machine Learning. Despite this simplifying ('naive') assumption, it is quick to train and often surprisingly accurate.