What is bias-variance tradeoff in Linear Regression?
Updated May 16, 2026
Short answer
It describes the tradeoff between model simplicity (bias) and flexibility (variance).
Deep explanation
High bias leads to underfitting, while high variance leads to overfitting. Linear regression typically has low bias but can have high variance with many features.
Real-world example
Balancing prediction accuracy in demand forecasting models.
Common mistakes
- Assuming reducing bias always improves performance.
Follow-up questions
- How does regularization affect variance?
- What is underfitting?