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Przeglądaj wybrane metody obok siebie; wiersze, które się różnią, są wyróżnione.
| Regresja metodą najmniejszych kwadratów (OLS)× | Regularyzacja grzbietowa (Ridge Regression)× | |
|---|---|---|
| Dziedzina≠ | Ekonometria | Uczenie maszynowe |
| Rodzina≠ | Regression model | Machine learning |
| Rok powstania≠ | 2019 | 1970 |
| Twórca≠ | Wooldridge (textbook treatment); classical least squares | Hoerl, A.E. & Kennard, R.W. |
| Typ≠ | Linear regression | L2-regularized linear regression |
| Źródło pierwotne≠ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 | Hoerl, A.E. & Kennard, R.W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67. DOI ↗ |
| Inne nazwy | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu | Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization |
| Pokrewne≠ | 5 | 4 |
| Podsumowanie≠ | Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE). | 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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