Methoden vergelijken
Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.
| Diagnostiek van invloed (Cook's distance, DFFITS, leverage)× | Ridge-regressie× | |
|---|---|---|
| Vakgebied≠ | Statistiek | Machine learning |
| Familie≠ | Regression model | Machine learning |
| Jaar van ontstaan≠ | 1977 | 1970 |
| Grondlegger≠ | R. Dennis Cook (Cook's distance); Belsley, Kuh & Welsch (DFFITS, leverage) | Hoerl, A.E. & Kennard, R.W. |
| Type≠ | Regression diagnostic | L2-regularized linear regression |
| Oorspronkelijke bron≠ | 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 ↗ |
| Aliassen≠ | Cook's distance, DFFITS, leverage, influential observation detection | Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization |
| Verwant≠ | 5 | 4 |
| Samenvatting≠ | 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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