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Explain K-Fold Cross-Validation and Its Trade-Offs

AmazonData Scientist · Technical Screen
MediumData ScientistTechnical ScreenMachine Learning

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This interview question evaluates core ML concepts, assumptions, math intuition, training/evaluation trade-offs, and practical failure modes in a realistic interview setting. A strong answer for Explain K-Fold Cross-Validation and Its Trade-Offs states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

Scenario

Phone interview focusing on breadth and depth of machine-learning knowledge.

Question

Explain the k-fold cross-validation procedure. What are the trade-offs in choosing the number of folds, and when might cross-validation give misleading performance estimates?

Hints

Address bias-variance, data leakage, temporal data, and class imbalance effects.