Handle multicollinearity in feature selection
Company: Travelers Insurance
Role: Data Scientist
Category: Machine Learning
Difficulty: easy
Interview Round: Technical Screen
You are building an interpretable predictive model for an insurance company, such as a linear or logistic regression model for claim risk. Several input features are highly correlated with one another.
Explain what **multicollinearity** is and why it can be a problem in modeling. In your answer, discuss:
1. How multicollinearity affects coefficient estimates, variance, statistical significance, and interpretability.
2. Whether multicollinearity harms prediction performance differently from inference or explanation.
3. How you would detect multicollinearity in practice.
4. How multicollinearity should influence your **feature selection** strategy.
5. What methods you could use to handle it, including tradeoffs among dropping variables, combining variables, regularization, and dimensionality reduction.
Assume the business wants both reasonable predictive performance and a model that stakeholders can interpret.
Quick Answer: This question evaluates understanding of multicollinearity and its implications for regression-based predictive models, specifically its effects on coefficient estimates, variance, statistical significance, and interpretability as they relate to feature selection.