Model Explainability Quiz

What you'll be asked

1. Which of the following best describes the goal of Model Explainability in MLOps?
2. You are using SHAP (SHapley Additive exPlanations) to interpret a model. What does a high positive SHAP value for a specific feature indicate?
3. When comparing LIME and SHAP for model explainability, which statement is most accurate?
4. Why is 'Global Explainability' important in an MLOps pipeline?
5. You observe that your model's explanation shows that a non-causal 'noise' feature is the primary driver of predictions. What is the most rigorous way to address this issue?

Answers and explanations come after you submit.