قارن الطرق
راجع الطرق التي اخترتها جنبًا إلى جنب؛ الصفوف المختلفة مميَّزة.
| شبكة المرونة (Elastic Net)× | انحدار ريدج (Ridge Regression)× | |
|---|---|---|
| المجال | تعلم الآلة | تعلم الآلة |
| العائلة | Machine learning | Machine learning |
| سنة النشأة≠ | 2005 | 1970 |
| صاحب الطريقة≠ | Zou, H. & Hastie, T. | Hoerl, A.E. & Kennard, R.W. |
| النوع≠ | Regularized linear regression (L1 + L2 penalty) | L2-regularized linear regression |
| المصدر التأسيسي≠ | 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 ↗ | Hoerl, A.E. & Kennard, R.W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67. DOI ↗ |
| الأسماء البديلة | Elastic Net Regresyon, elastic net regression, ElasticNet, L1/L2 regularized regression | Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization |
| ذات صلة | 4 | 4 |
| الملخص≠ | 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. | 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. |
| ScholarGateمجموعة البيانات ↗ |
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