Your question is Accuracy vs Interpretability. Take a moment with it on the right.
Talk me through your thinking if you like. When you're confident, submit your answer and I'll grade it like a real screen (7/10 or better passes).
How do you balance the trade-off between model accuracy and model interpretability in a banking context?
Explain how you would choose between an interpretable model, such as logistic regression or a shallow decision tree, and a more complex model, such as gradient boosting. Discuss validation, explainability, fairness, calibration, monitoring, and how the decision changes with the risk of the banking use case. Include a practical modeling and evaluation approach rather than only defining interpretability.
