A company makes forecasts each quarter to decide how to optimize operations to meet expected demand. The company uses ML models to make these forecasts. An AI practitioner is writing a report about the trained ML models to provide transparency and explainability to company stakeholders. What should the AI practitioner include in the report to meet the transparency and explainability requirements?
Show Answer & Explanation
Correct Answer: B. Partial dependence plots (PDPs)
Partial dependence plots (PDPs) are visualization tools that show the marginal effect of features on the predicted outcome of a model. They help stakeholders understand how different input variables influence the model's predictions. PDPs are ideal for transparency and explainability because they provide intuitive visual explanations of model behavior without requiring technical expertise. Code for model training and convergence tables are too technical for stakeholders. Sample data alone does not explain model behavior.