You have 500 candidate features for a churn model. How do you decide which ones to keep?

What the interviewer wants to hear

• Remove leaky, constant and duplicate features first
• Use domain knowledge
• Model-based importance, regularisation, permutation importance
• Validate with cross-validation, not training performance
• Consider cost, latency and stability of features

Model answer

First I clean up: remove features that leak the target, such as anything recorded after the churn date, plus constant, nearly empty and duplicate columns. I check pairs with very high correlation and keep the more reliable one. Domain knowledge helps me group features into themes, like usage, billing and support. Then I let a model help. With gradient boosting I look at permutation importance or SHAP values on validation data, since built-in split importance can be misleading. L1 regularisation in a logistic model is another way to shrink weak features to zero. I always judge choices by cross-validated performance, not training scores, and check the selection is stable across folds. Finally I consider practicality: features that are expensive or slow to compute in production, or hard to explain, need to earn their place.

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