Study
Machine Learning · Regularisation: ridge and lasso
Overfitting with too many features, and how L2 (ridge) shrinks weights while L1 (lasso) drives some exactly to zero.
- Regularisation adds a penalty on weight size so the model prefers simpler fits.
- Lasso's L1 penalty sets some weights to exactly zero, which is a built-in feature selection.