seniorSupervised Learning
What is regularization path in supervised learning?
Updated May 17, 2026
Short answer
A regularization path shows how model coefficients change as regularization strength varies.
Deep explanation
As regularization strength changes, model weights shrink or grow. The regularization path visualizes this transition, helping understand feature importance and model stability. It is commonly used in Lasso and Ridge regression to analyze how features enter or leave the model.
Real-world example
Selecting important features in medical datasets using Lasso paths.
Common mistakes
- Interpreting unstable coefficients as model failure rather than regularization effect.
Follow-up questions
- What happens at high alpha values?
- Why is Lasso useful for feature selection?