What is multicollinearity and how to fix it?
Updated May 16, 2026
Short answer
It is high correlation among independent variables, which destabilizes coefficients.
Deep explanation
It inflates variance of coefficient estimates and makes interpretation unreliable. It can be detected using VIF and resolved via feature removal or regularization.
Real-world example
Age and experience in salary prediction datasets.
Common mistakes
- Keeping redundant variables unnecessarily.
Follow-up questions
- What is a high VIF threshold?
- Does ridge regression solve it?