ElasticNet Regression
Linear regression combining L1 and L2 regularization
ElasticNet combines Lasso's L1 sparsity with Ridge's L2 stability. It is useful when features are correlated and you want both feature selection and coefficient shrinkage.
When to use:
- Correlated features where Lasso arbitrarily picks one from a group
- High-dimensional problems needing both sparsity and stability
- When the right balance between Lasso and Ridge is unclear
Input: Tabular data with the feature columns defined during training Output: Continuous predicted value
Model Settings (set during training, used at inference)
Alpha (default: 1.0) Overall regularization strength.
L1 Ratio (default: 0.5)
Mixing ratio between L1 and L2. 0 is pure Ridge, 1 is pure Lasso, 0.5 is equal mix.
Max Iter (default: 1000) Maximum iterations for convergence.
Fit Intercept (default: true) Whether to include a bias term.
Inference Settings
No dedicated inference-time settings.