Сравнение на методи
Прегледайте избраните методи един до друг; редовете с разлики са откроени.
| Регресия с гребен (Ridge Regression)× | Elastic Net× | |
|---|---|---|
| Област | Машинно обучение | Машинно обучение |
| Семейство | Machine learning | Machine learning |
| Година на възникване≠ | 1970 | 2005 |
| Създател≠ | Hoerl, A.E. & Kennard, R.W. | Zou, H. & Hastie, T. |
| Тип≠ | L2-regularized linear regression | Regularized linear regression (L1 + L2 penalty) |
| Основополагащ източник≠ | Hoerl, A.E. & Kennard, R.W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67. DOI ↗ | Zou, H. & Hastie, T. (2005). Regularization and Variable Selection via the Elastic Net. Journal of the Royal Statistical Society: Series B, 67(2), 301–320. DOI ↗ |
| Други названия | Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization | Elastic Net Regresyon, elastic net regression, ElasticNet, L1/L2 regularized regression |
| Свързани | 4 | 4 |
| Резюме≠ | 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. | Elastic Net is a regularized linear regression method introduced by Zou and Hastie in 2005 that blends the LASSO (L1) and Ridge (L2) penalties, so it performs variable selection and coefficient shrinkage at the same time. It is designed for predictive and explanatory modelling on data with many, possibly correlated, predictors. |
| ScholarGateНабор от данни ↗ |
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