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| Support Vector Regression× | Lasso-Regression× | Ridge Regression× | |
|---|---|---|---|
| Fachgebiet | Maschinelles Lernen | Maschinelles Lernen | Maschinelles Lernen |
| Familie | Machine learning | Machine learning | Machine learning |
| Entstehungsjahr≠ | 2004 | 1996 | 1970 |
| Urheber≠ | Smola, A.J. & Schölkopf, B. | Tibshirani, R. | Hoerl, A.E. & Kennard, R.W. |
| Typ≠ | Kernel-based supervised model (epsilon-insensitive regression) | Regularized linear regression (L1 penalty) | L2-regularized linear regression |
| Wegweisende Quelle≠ | Smola, A.J. & Schölkopf, B. (2004). A Tutorial on Support Vector Regression. Statistics and Computing, 14, 199–222. DOI ↗ | 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 ↗ |
| Aliasnamen | Destek Vektör Regresyonu (SVR), SVR, epsilon-SVR, support vector machine for regression | LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization | Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization |
| Verwandt | 4 | 4 | 4 |
| Zusammenfassung≠ | Support Vector Regression (SVR), described in Smola and Schölkopf's 2004 tutorial, predicts a continuous outcome by fitting a function that stays within an epsilon-wide tube around the data while incurring as little error as possible. It extends the support vector machine idea from classification to regression, using a kernel to capture nonlinear relationships. | 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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