Porównaj metody
Przeglądaj wybrane metody obok siebie; wiersze, które się różnią, są wyróżnione.
| Regresja logistyczna× | Maszyna wektorów nośnych (klasyfikacja)× | |
|---|---|---|
| Dziedzina≠ | Statystyka w badaniach | Uczenie maszynowe |
| Rodzina≠ | Process / pipeline | Machine learning |
| Rok powstania≠ | 1958 | 1995 |
| Twórca≠ | David Roxbee Cox | Cortes, C. & Vapnik, V. |
| Typ≠ | Method | Maximum-margin classifier (kernel method) |
| Źródło pierwotne≠ | Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242. DOI ↗ | Cortes, C. & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20, 273–297. DOI ↗ |
| Inne nazwy≠ | logit model, binomial logistic regression, LR | Destek Vektör Makinesi (SVM — Sınıflandırma), support-vector network, SVM classifier, maximum-margin classifier |
| Pokrewne≠ | 3 | 5 |
| Podsumowanie≠ | 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. | The Support Vector Machine, introduced by Corinna Cortes and Vladimir Vapnik in 1995, is a classifier that finds the optimal separating hyperplane between classes in a high-dimensional space. It chooses the boundary that leaves the widest possible margin to the nearest training points, which makes its decisions robust on new data. |
| ScholarGateZbiór danych ↗ |
|
|