What is Regularization in Logistic Regression?
Updated May 17, 2026
Short answer
Regularization prevents overfitting by penalizing large coefficients.
Deep explanation
L1 regularization promotes sparsity, while L2 shrinks coefficients smoothly. Regularization improves generalization.
Real-world example
Marketing models reduce noise using L1 regularization.
Common mistakes
- Applying strong regularization without tuning.
Follow-up questions
- What is ElasticNet?
- Why use L1?