I talked through a few angles: checking variance inflation, dropping one of a correlated pair based on domain knowledge, or just letting regularization handle it.
Start by explaining that correlation itself isn't inherently bad, but it can cause multicollinearity, making model interpretation and feature importance unreliable. Then outline a systematic process: assess correlation, consider model type, use feature selection techniques, and validate with domain knowledge. Emphasize that the decision should be driven by the model's goal and the trade-off between simplicity and performance.
Pro tip: Mention that for tree-based models, correlated features are less problematic, but for linear models, you might use regularization (Lasso) to automatically select features. Also, highlight that sometimes keeping both can improve performance if they capture different aspects, so always validate empirically.
Compute correlation matrix (Pearson, Spearman) and VIF scores to identify highly correlated features. Determine the severity and potential impact on the model.
For linear models, multicollinearity is a concern; for tree-based models, it's less so. Also, consider if interpretability or predictive performance is the priority.
Use methods like Lasso regularization, recursive feature elimination, or PCA to reduce dimensionality. Alternatively, keep the feature with higher variance or better domain relevance.
Consult domain experts to decide which feature is more meaningful. Run experiments with different feature subsets and compare model performance using cross-validation.
After deployment, monitor model performance and feature importance. Be prepared to revisit feature selection if data distribution changes.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.