Explain K-Fold Cross-Validation and Its Trade-Offs
Company: Amazon
Role: Data Scientist
Category: Machine Learning
Difficulty: medium
Interview Round: Technical Screen
##### 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.
Quick Answer: 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.
