Elastic Net combines L1 and L2 penalties to control variance and select features when predictors are correlated—often outperforming pure Lasso or Ridge in such settings.
Tuning Parameters
- alpha: overall penalty strength (higher = more shrinkage)
- l1_ratio: mix between L1 and L2 (0 = Ridge, 1 = Lasso)
- Use cross‑validation to select both; inspect coefficient paths and validation errors
Preprocessing
- Scale features (z‑score) and encode categoricals
- Handle missing values; consider interaction terms if domain suggests
Diagnostics
- Residual plots and error metrics (RMSE/MAE/R²)
- Coefficient paths vs alpha; stability across folds
- Permutation importance or standardized coefficients for interpretation
When to Prefer Elastic Net
- Groups of correlated predictors where Lasso is unstable and Ridge is dense
- Need a balance of sparsity and stability