Methoden vergleichen

	Erklärbarer Naive Bayes	Logistische Regression	Naive Bayes	Random Forest
Fachgebiet	Maschinelles Lernen	Forschungsstatistik	Maschinelles Lernen	Maschinelles Lernen
Familie	Machine learning	Process / pipeline	Machine learning	Machine learning
Entstehungsjahr	1950s (Naive Bayes); 2000s–2010s (explainability focus)	1958	1997	2001
Urheber	Zhang, H. (explainability framing); Naive Bayes: Good, I. J.	David Roxbee Cox	Mitchell, T. M. (textbook treatment)	Breiman, L.
Typ	Probabilistic generative classifier with intrinsic explainability	Method	Probabilistic classifier (Bayes' theorem with conditional independence)	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).	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	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Aliasnamen	XNB, interpretable Naive Bayes, transparent Naive Bayes, explainable probabilistic classifier	logit model, binomial logistic regression, LR	Naive Bayes Sınıflandırıcı, naive bayes classifier, simple Bayes, Gaussian Naive Bayes	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Verwandt	4	3	4	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.	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.	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.