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| 영향력 진단 (쿡 거리, DFFITS, 레버리지)× | 릿지 회귀(Ridge Regression)× | |
|---|---|---|
| 분야≠ | 통계학 | 머신러닝 |
| 계열≠ | Regression model | Machine learning |
| 기원 연도≠ | 1977 | 1970 |
| 창시자≠ | R. Dennis Cook (Cook's distance); Belsley, Kuh & Welsch (DFFITS, leverage) | Hoerl, A.E. & Kennard, R.W. |
| 유형≠ | Regression diagnostic | L2-regularized linear regression |
| 원전≠ | Cook, R. D. (1977). Detection of Influential Observations in Linear Regression. Technometrics, 19(1), 15-18. DOI ↗ | Hoerl, A.E. & Kennard, R.W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67. DOI ↗ |
| 별칭≠ | Cook's distance, DFFITS, leverage, influential observation detection | Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization |
| 관련≠ | 5 | 4 |
| 요약≠ | Influence diagnostics are a family of post-fit measures that quantify how much each single observation affects a fitted regression. Cook's distance was introduced by R. Dennis Cook in 1977, with leverage and DFFITS formalised by Belsley, Kuh and Welsch in 1980, to flag the observations that most strongly pull the estimated coefficients. | 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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