How does L1 and L2 regularization help in high dimensions?
Updated May 15, 2026
Short answer
They penalize complexity to prevent overfitting.
Deep explanation
L1 performs feature selection by driving weights to zero; L2 shrinks weights smoothly, stabilizing models.
Real-world example
Financial models selecting key indicators.
Common mistakes
- Assuming regularization removes all noise.
Follow-up questions
- L1 vs L2 difference?
- Can they be combined?