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| Wyjaśnialny Naiwny Klasyfikator Bayesowski× | Regresja logistyczna× | |
|---|---|---|
| Dziedzina≠ | Uczenie maszynowe | Statystyka w badaniach |
| Rodzina≠ | Machine learning | Process / pipeline |
| Rok powstania≠ | 1950s (Naive Bayes); 2000s–2010s (explainability focus) | 1958 |
| Twórca≠ | Zhang, H. (explainability framing); Naive Bayes: Good, I. J. | David Roxbee Cox |
| Typ≠ | Probabilistic generative classifier with intrinsic explainability | Method |
| Źródło pierwotne≠ | Rish, I. (2001). An empirical study of the naive Bayes classifier. In IJCAI Workshop on Empirical Methods in AI (pp. 41–46). link ↗ | Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242. DOI ↗ |
| Inne nazwy≠ | XNB, interpretable Naive Bayes, transparent Naive Bayes, explainable probabilistic classifier | logit model, binomial logistic regression, LR |
| Pokrewne≠ | 4 | 3 |
| Podsumowanie≠ | 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. |
| ScholarGateZbiór danych ↗ |
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