Porovnat metody
Prohlédněte si vybrané metody vedle sebe; řádky, které se liší, jsou zvýrazněny.
| Logistická regrese (ML)× | Naive Bayes× | |
|---|---|---|
| Obor | Strojové učení | Strojové učení |
| Rodina | Machine learning | Machine learning |
| Rok vzniku≠ | 1958 | 1997 |
| Tvůrce≠ | Cox, D. R. | Mitchell, T. M. (textbook treatment) |
| Typ≠ | Probabilistic linear classifier | Probabilistic classifier (Bayes' theorem with conditional independence) |
| Původní zdroj≠ | Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242. DOI ↗ | Mitchell, T. M. (1997). Machine Learning. McGraw-Hill. ISBN: 978-0070428072 |
| Další názvy≠ | logit model, logit regression, binomial logistic regression, maximum entropy classifier | Naive Bayes Sınıflandırıcı, naive bayes classifier, simple Bayes, Gaussian Naive Bayes |
| Příbuzné≠ | 5 | 4 |
| Shrnutí≠ | Logistic regression is a foundational probabilistic classifier that models the log-odds of a binary (or multinomial) outcome as a linear function of the predictors. Introduced by D. R. Cox in 1958, it remains one of the most widely used and interpretable classification methods in both statistics and machine learning, valued for its calibrated probability outputs and clear coefficient interpretation. | 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. |
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