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| 엘라스틱 넷 회귀× | 정규화 로지스틱 회귀× | |
|---|---|---|
| 분야≠ | 통계학 | 머신러닝 |
| 계열≠ | Regression model | Machine learning |
| 기원 연도≠ | 2005 | 1996–2005 |
| 창시자≠ | Hui Zou and Trevor Hastie | Tibshirani, R. (lasso); Hoerl & Kennard (ridge); Zou & Hastie (elastic net) |
| 유형≠ | Penalized linear regression | Penalized classification model |
| 원전≠ | 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 ↗ |
| 별칭 | 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 |
| 관련≠ | 6 | 5 |
| 요약≠ | 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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