Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Regulētā lineārā regresija (Ridge Regression)× | Logistiskā regresija× | |
|---|---|---|
| Nozare≠ | Mašīnmācīšanās | Pētniecības statistika |
| Saime≠ | Machine learning | Process / pipeline |
| Izcelsmes gads≠ | 1970 | 1958 |
| Autors≠ | Hoerl, A.E. & Kennard, R.W. | David Roxbee Cox |
| Tips≠ | L2-regularized linear regression | Method |
| Pirmavots≠ | Hoerl, A.E. & Kennard, R.W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67. DOI ↗ | Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242. DOI ↗ |
| Citi nosaukumi≠ | Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization | logit model, binomial logistic regression, LR |
| Saistītās≠ | 4 | 3 |
| Kopsavilkums≠ | 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. | 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. |
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