Your question is Making ML Models Explainable. 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).
Explain methods or techniques you use to make a machine learning model explainable.
Discuss both inherently interpretable models and post-hoc explanation methods. Cover global and local explanations, how you validate that explanations are faithful, and important limitations such as correlated features, leakage, instability, and misleading feature importance. Use a practical example or code where appropriate.
