Methoden vergleichen

	Erklärbarer Naive Bayes	Entscheidungsbaum	Logistische Regression	Random Forest
Fachgebiet	Maschinelles Lernen	Maschinelles Lernen	Forschungsstatistik	Maschinelles Lernen
Familie	Machine learning	Machine learning	Process / pipeline	Machine learning
Entstehungsjahr	1950s (Naive Bayes); 2000s–2010s (explainability focus)	1984	1958	2001
Urheber	Zhang, H. (explainability framing); Naive Bayes: Good, I. J.	Breiman, Friedman, Olshen & Stone	David Roxbee Cox	Breiman, L.
Typ	Probabilistic generative classifier with intrinsic explainability	Recursive partitioning (if-then rules)	Method	Ensemble (bagging of decision trees)
Wegweisende Quelle	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.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Aliasnamen	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	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Verwandt	4	5	3	4
Zusammenfassung	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.	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.