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Granska de valda metoderna sida vid sida; rader som skiljer sig är markerade.
| Elastic Net-regression× | Regulariserad logistisk regression× | |
|---|---|---|
| Ämnesområde≠ | Statistik | Maskininlärning |
| Familj≠ | Regression model | Machine learning |
| Ursprungsår≠ | 2005 | 1996–2005 |
| Upphovsperson≠ | Hui Zou and Trevor Hastie | Tibshirani, R. (lasso); Hoerl & Kennard (ridge); Zou & Hastie (elastic net) |
| Typ≠ | Penalized linear regression | Penalized classification model |
| Ursprungskälla≠ | Zou, H., & Hastie, T. (2005). Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 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 ↗ |
| Alias | elastic net, EN regression, L1+L2 regularized regression, combined lasso-ridge regression | penalized logistic regression, L1 logistic regression, L2 logistic regression, elastic net logistic regression |
| Närliggande≠ | 6 | 5 |
| Sammanfattning≠ | Elastic net regression combines the L1 (lasso) and L2 (ridge) penalties into a single regularized regression framework. Controlled by a mixing parameter alpha and a shrinkage strength lambda, it can simultaneously select variables and handle correlated predictors — overcoming key limitations of pure lasso and pure ridge applied alone. | Regularized logistic regression extends standard logistic regression by adding an L1 (lasso), L2 (ridge), or elastic net penalty to the log-likelihood, shrinking coefficients toward zero and preventing overfitting. It is the default choice for binary or multinomial classification when you want interpretable, sparse, or stable coefficient estimates in high-dimensional or collinear feature spaces. |
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