Detect and address multicollinearity
Company: OneMain Financial
Role: Data Scientist
Category: Statistics & Math
Difficulty: easy
Interview Round: Technical Screen
## Prompt
You fit a linear/logistic regression model and suspect **multicollinearity** among features.
1. What is multicollinearity and why is it a problem?
2. How would you **detect** it (specific diagnostics)?
3. How would you **address** it (practical remedies)?
4. What changes in interpretation/performance should you expect after fixing it?
Quick Answer: This question evaluates understanding of multicollinearity as a statistical concept and the competency to recognize its presence, interpret its effects on regression coefficients, and consider appropriate mitigation strategies.