VIF I knew cold, regress each feature on the rest and use 1/(1-R^2).
Explain that VIF is computed as 1/(1-R²) from regressing each feature on all others, and for binary features this is equivalent to the inverse of (1 - φ²) where φ is the phi coefficient. Note that a correlation of 0.98 implies R² ≈ 0.96, so VIF ≈ 25, which exceeds common thresholds of 5 or 10, indicating problematic multicollinearity.
Pro tip: Mention that for binary features, the correlation is the phi coefficient, and VIF can be computed directly from the 2x2 contingency table without running a regression. Also, note that in practice, you might drop one feature or combine them, but always validate with domain knowledge.
State that VIF for feature i is 1/(1 - R_i²), where R_i² is the R-squared from regressing feature i on all other features. For two features, R_i² is the squared correlation between them.
Given correlation r = 0.98, compute R² = r² = 0.9604. This is the proportion of variance in one feature explained by the other.
Plug into formula: VIF = 1/(1 - 0.9604) = 1/0.0396 ≈ 25.25. So each feature has VIF ≈ 25.
Use common thresholds: VIF > 5 or 10 indicates problematic multicollinearity. Here VIF ≈ 25 far exceeds these, so flag as problematic.
Explain that high VIF can inflate coefficient variance and make interpretation unreliable. Suggest remedies: drop one feature, combine them, or use regularization.
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Start by defining interventional and conditional SHAP in terms of their underlying assumptions about feature dependence. Then explain how near-duplicate features violate these assumptions differently, leading to distinct attribution behaviors. Finally, discuss the practical implications for model interpretation and stability.
Pro tip: Emphasize that conditional SHAP can split credit arbitrarily among correlated features due to the conditional expectation, while interventional SHAP may assign credit to one feature based on the data distribution, but both can be unstable. Mention that using SHAP with correlated features requires caution and often domain knowledge to interpret correctly.
Explain that interventional SHAP (also known as marginal SHAP) breaks feature dependencies by perturbing features independently, while conditional SHAP (also known as observational SHAP) respects the data distribution by conditioning on other features.
With near-duplicate features, interventional SHAP may assign all credit to one feature if the model relies on it, but if both are used, it can split credit based on marginal contributions. Conditional SHAP, however, may split credit more evenly because it accounts for the fact that when one feature is present, the other is likely present, leading to arbitrary splits.
Instability arises because small changes in the data or model can shift which feature gets credit, especially under conditional SHAP where the conditional distribution is estimated. Interventional SHAP can also be unstable if the model uses both features in a complementary way, as perturbations may create unrealistic data points.
Highlight that this instability makes interpretation challenging, and suggest strategies like grouping correlated features, using domain knowledge, or employing alternative methods like SHAP with correlation-aware adjustments.
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Talked about clustering features by correlation first, then grouped SHAP to treat A and B as a unit, then permutation importance conditioned on the other feature to see if one is redundant.
Start by framing the problem as a multicollinearity issue that affects model interpretability and stability, then outline a step-by-step diagnostic workflow. Emphasize that the goal is to decide whether to drop, combine, or keep both features based on their incremental value and business context.
Pro tip: Always tie the decision back to the model's purpose: if interpretability matters (e.g., for stakeholder trust), favor dropping one; if pure predictive power is key, consider regularization or feature engineering. Mention that you'd validate with a holdout set to ensure no performance loss.
Compute correlation (Pearson, Spearman) and check for perfect or near-perfect linear dependence. Also inspect scatterplots and calculate VIF to confirm multicollinearity.
Fit the model with both features and examine coefficient stability, standard errors, and feature importance. Compare with models using only one feature to see if performance drops.
Determine if the features are semantically redundant or if they capture different nuances (e.g., time-decayed vs. raw counts). Consult with domain experts to understand if both are needed for interpretability.
Decide among: dropping one feature, combining them (e.g., PCA, averaging), or using regularization (L1/L2) to handle collinearity. Justify based on model goals and interpretability needs.
Test the chosen approach on a holdout set and compare metrics. Set up monitoring to detect if the relationship between features changes over time, which could affect the decision.
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Elastic net for the GLM was the obvious answer and I led with that.
Start by defining near-duplicate features and their impact on model stability and interpretability. Then, for each model type, propose tailored strategies: for logistic regression, use regularization or feature clustering; for GBT, leverage feature importance and drop one of each correlated pair. Finally, outline a validation plan that compares predictive performance (e.g., AUC, log loss) and interpretability metrics (e.g., feature importance stability) using cross-validation and holdout sets.
Pro tip: Emphasize that near-duplicates can cause multicollinearity in logistic regression, inflating variance, and in GBT, they can split importance arbitrarily. Propose a combined approach: use correlation-based pruning and then validate with a business metric to ensure no performance drop.
Compute pairwise correlations (Pearson, Spearman) and use variance inflation factor (VIF) for linear models. For GBT, use feature importance and SHAP values to detect redundancy.
For logistic regression: use L1/L2 regularization, drop one of each correlated pair, or combine via PCA. For GBT: drop redundant features based on importance, or use feature selection algorithms like Boruta.
Use cross-validation to compare models with and without changes on metrics like AUC, log loss, and calibration. Perform statistical tests (e.g., DeLong's test) to ensure differences are not significant.
Measure feature importance stability across bootstrapped samples, and check for reduced multicollinearity (e.g., lower VIF). Use SHAP dependence plots to ensure simpler explanations.
If performance drops, consider keeping some duplicates or using domain knowledge to combine features. Document the rationale and monitor model in production.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.