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| Regresija Lasso× | Ridge Regression× | |
|---|---|---|
| Područje | Strojno učenje | Strojno učenje |
| Obitelj | Machine learning | Machine learning |
| Godina nastanka≠ | 1996 | 1970 |
| Tvorac≠ | Tibshirani, R. | Hoerl, A.E. & Kennard, R.W. |
| Vrsta≠ | Regularized linear regression (L1 penalty) | L2-regularized linear regression |
| Temeljni izvor≠ | Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288. DOI ↗ | Hoerl, A.E. & Kennard, R.W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67. DOI ↗ |
| Drugi nazivi | LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization | Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization |
| Srodne | 4 | 4 |
| Sažetak≠ | Lasso regression, introduced by Robert Tibshirani in 1996, is a linear regression method that adds an L1 penalty to the loss so that it shrinks coefficients and performs variable selection at the same time, producing a sparse model. By driving some coefficients exactly to zero it keeps only the predictors that matter. | 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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