Your question is Interpreting Model Decisions Clearly. 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).
You have a model that is performing well enough to be used by analysts, but stakeholders are asking how they can trust its decisions. The team wants explanations that are understandable, consistent, and useful for reviewing individual predictions as well as overall behavior.
How do you ensure that your machine learning models are interpretable?