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| Regresja logistyczna× | Regularyzacja grzbietowa (Ridge Regression)× | |
|---|---|---|
| Dziedzina≠ | Statystyka w badaniach | Uczenie maszynowe |
| Rodzina≠ | Process / pipeline | Machine learning |
| Rok powstania≠ | 1958 | 1970 |
| Twórca≠ | David Roxbee Cox | Hoerl, A.E. & Kennard, R.W. |
| Typ≠ | Method | L2-regularized linear regression |
| 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 ↗ | Hoerl, A.E. & Kennard, R.W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67. DOI ↗ |
| Inne nazwy≠ | logit model, binomial logistic regression, LR | Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization |
| Pokrewne≠ | 3 | 4 |
| 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. | Ridge Regression is an L2-regularized linear regression method, introduced by Arthur Hoerl and Robert Kennard in 1970, that reduces multicollinearity by adding a penalty on the size of the coefficients. It shrinks coefficients toward zero without setting any of them exactly to zero, producing more stable estimates when predictors are highly correlated. |
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