विधियों की तुलना करें
चुनी हुई विधियों की आमने-सामने समीक्षा करें; भिन्नता वाली पंक्तियाँ रेखांकित हैं।
| इलास्टिक नेट× | लासो रिग्रेशन× | |
|---|---|---|
| क्षेत्र | मशीन अधिगम | मशीन अधिगम |
| परिवार | Machine learning | Machine learning |
| उद्भव वर्ष≠ | 2005 | 1996 |
| प्रवर्तक≠ | Zou, H. & Hastie, T. | Tibshirani, R. |
| प्रकार≠ | Regularized linear regression (L1 + L2 penalty) | Regularized linear regression (L1 penalty) |
| मौलिक स्रोत≠ | 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 ↗ | Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288. DOI ↗ |
| उपनाम | Elastic Net Regresyon, elastic net regression, ElasticNet, L1/L2 regularized regression | LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization |
| संबंधित | 4 | 4 |
| सारांश≠ | 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. | 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. |
| ScholarGateडेटासेट ↗ |
|
|