L1 vs L2 Regularization: Prevent Overfitting in ML
Updated on January 30, 2026 5 minutes read
Frequently Asked Questions
What’s the difference between L1 and L2 regularization?
L1 adds a penalty based on absolute coefficients and often produces sparse models (some coefficients become zero). L2 adds a penalty based on squared coefficients and usually shrinks weights without zeroing them.
How do I choose the regularization strength (λ or alpha)?
Treat it as a hyperparameter. Tune it with a validation set or cross-validation, then confirm performance on a separate held-out test set to avoid overfitting your evaluation.
Is Elastic Net better than Lasso or Ridge?
Not always. Elastic Net is often helpful when you want some sparsity, but your features are correlated. If you mainly want stability, L2 can be a simpler default; if you mainly want feature selection, L1 can be enough.