Model Evaluation and Quality Assessment Questions

Covers evaluation methods, metrics, and quality assessment approaches for machine learning models including both predictive models and generative models. Topics include selecting appropriate metrics such as accuracy, precision, recall, F one score, area under curve for ranking, root mean square error and mean absolute percentage error for regression, and the rationale for using multiple metrics and baselines. For generative and large language models, covers automatic metrics such as BLEU, ROUGE, METEOR, semantic similarity scores, LLM based evaluation techniques, human evaluation frameworks, factuality and hallucination checking, adversarial and stress testing, error analysis, and designing scalable, cost effective evaluation pipelines and quality assurance processes.

HardTechnical

105 practiced

How would you evaluate fairness metrics across many intersectional subgroups (e.g., race × gender × age) when some subgroups have very small sample sizes? Propose statistical techniques and reporting practices to avoid drawing false conclusions while ensuring responsible transparency.

HardTechnical

71 practiced

Design an adversarial testing strategy for image classification models in production. Consider attack types: pixel-level noise, geometric transformations (rotation/scale), distributional shifts (lighting, sensors), and adversarial examples (PGD/CW). Describe how to measure robustness, generate adversarial datasets, and integrate defenses and robustness checks into the evaluation lifecycle.

EasyTechnical

72 practiced

Given this confusion matrix on 10,000 examples: TP=300, FP=100, TN=8400, FN=200 — compute accuracy, precision, recall, F1, and false positive rate. After computing, explain how class imbalance affects accuracy and why accuracy can be misleading in many business contexts.

MediumTechnical

68 practiced

Describe group k-fold cross-validation and provide a concrete scenario where it is necessary (for example user-level or batch-level correlation). Include a short Python example using sklearn's GroupKFold that demonstrates how to split data by group IDs.

MediumTechnical

66 practiced

Define model calibration and explain how to evaluate calibration for probabilistic classifiers. Describe reliability diagrams, the Brier score, and how you would apply Platt scaling or isotonic regression to fix miscalibration. Mention pitfalls when calibrating on small validation sets.

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