AI, data and statistics

Model Evaluation interview questions

Leakage prevention, independent validation and interpretation of metrics.

6 questions, each with a worked answer

About Model Evaluation

Model evaluation asks whether a model will perform as claimed on the data it will actually meet. It covers splitting data correctly, choosing metrics that match the decision, and testing on truly independent sets. Leakage, where information from the test set reaches training, is the most common way results are inflated.

In regulated and scientific settings, the evaluation is often scrutinised more closely than the model itself.

Fundamentals: 1
Intermediate: 2
Advanced: 3

What these questions test

Evaluate biomedical models: Choose metrics, validation splits and subgroup analyses for realistic generalization.

References

Common pitfalls and recommended practices. scikit-learn User Guide. scikit-learn.org/stable/common_pitfalls.html.
scikit-learn.org/stable/modules/model_evaluation.html.
journals.plos.org/plosone/article?id=10.1371/journal.pone.0118432.
scikit-learn.org/stable/auto_examples/calibration/plot_calibration_curve.html.
scikit-learn.org/stable/modules/classification_threshold.html.
bmj.com/content/385/bmj-2023-078378.

Practice material written by TheBench.Bio and checked against the references above, reviewed October 23, 2026. Written to practise with, not collected from real interviews.