How do you detect and fix multicollinearity?
Company: IBM
Role: Data Scientist
Category: Statistics & Math
Difficulty: easy
Interview Round: Technical Screen
In a regression-style model (e.g., linear regression or logistic regression) you suspect multicollinearity among features.
1) What is multicollinearity and why is it a problem?
2) How would you detect it (diagnostics/plots/statistics)?
3) How would you mitigate it in practice while preserving predictive performance and/or interpretability?
4) How does the answer differ for inference (understanding coefficients) vs pure prediction?
Overview: This question evaluates understanding of multicollinearity and related competencies in regression diagnostics, feature engineering, model interpretability, and the trade-offs between coefficient inference and predictive performance.
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