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| 최소제곱법(OLS) 회귀× | 릿지 회귀(Ridge Regression)× | |
|---|---|---|
| 분야≠ | 계량경제학 | 머신러닝 |
| 계열≠ | Regression model | Machine learning |
| 기원 연도≠ | 2019 | 1970 |
| 창시자≠ | Wooldridge (textbook treatment); classical least squares | Hoerl, A.E. & Kennard, R.W. |
| 유형≠ | Linear regression | L2-regularized linear regression |
| 원전≠ | 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 ↗ |
| 별칭 | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu | Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization |
| 관련≠ | 5 | 4 |
| 요약≠ | 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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