ML Model Evaluation Interview Questions

Common model evaluation patterns

• Choosing between precision and recall based on business impact
• Interpreting AUC-ROC and precision-recall curves
• Cross-validation strategies (k-fold, stratified, time-series)
• Diagnosing overfitting vs underfitting from learning curves
• Evaluating models on imbalanced datasets
• A/B testing ML models in production

Common mistakes in model evaluation

• Using accuracy as the primary metric on imbalanced datasets
• Not considering the business cost of false positives vs false negatives
• Overfitting to the validation set through excessive hyperparameter tuning
• Ignoring data leakage in cross-validation splits
• Evaluating offline metrics without considering online performance

How model evaluation questions are assessed

Related ML concepts

• Feature engineering interview questions
• ML system design interview questions