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Design approach for class imbalance

NewsBreakMachine Learning Engineer · Technical Screen
HardMachine Learning EngineerTechnical 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 Design approach for class imbalance states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

You are training a highly imbalanced binary classifier. Explain the impact of class imbalance on learning and evaluation. Compare strategies including random over/under-sampling, synthetic methods (e.g., SMOTE/ADASYN), class-weighting, focal loss, and threshold moving. Describe how to structure cross-validation to avoid leakage (e.g., perform resampling within each training fold only), choose appropriate metrics (e.g., PR AUC, recall at fixed precision, balanced accuracy), and tune hyperparameters. Discuss trade-offs in variance, bias, runtime, and calibration.