Explain SHAP vs VIF under collinearity

Q: Explain SHAP vs VIF under collinearity

This question evaluates understanding of multicollinearity diagnostics (VIF), feature attribution methods (SHAP), and the interpretability and validation implications of near-duplicate predictors in binary classification models.

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This is a hard difficulty Machine Learning question, commonly asked during Technical Screen rounds at TikTok.

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Question

High Collinearity in Binary Classification: VIF, SHAP, and Interpretation Strategy

You are modeling a binary outcome Y. Two numeric features A and B are highly correlated: corr(A, B) = 0.98. Other features exist but are only weakly correlated with A and B. You fit:

(i) a logistic regression (GLM), and
(ii) a gradient-boosted tree model (GBDT).

Answer the following:

Variance Inflation Factor (VIF)
- Compute/estimate the VIF for A and B given corr(A, B) = 0.98 (assume other features add little to R²).
- Interpret typical VIF/tolerance thresholds that indicate problematic multicollinearity and what that means for logistic regression coefficients.
SHAP with near-duplicate features
- Explain how SHAP values behave when A and B are near-duplicates under interventional vs conditional SHAP formulations.
- Why can attributions be unstable or split unpredictably across A and B (for both GLM and GBDT)?
Defensible interpretation workflow
- Propose a workflow to interpret such models: e.g., feature clustering and grouped SHAP, permutation importance conditional on the other feature, and re-fitting after removing one of the pair.
- Describe the diagnostics you expect to see if A and B are redundant.
Modeling recommendations and validation
- Recommend modeling changes (e.g., elastic net for GLM; feature grouping or regularization choices for trees) to handle collinearity.
- Describe how you would validate that both interpretability and predictive performance remain acceptable.

Explain SHAP vs VIF under collinearity

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Explain SHAP vs VIF under collinearity

High Collinearity in Binary Classification: VIF, SHAP, and Interpretation Strategy

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