Question
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?
- A. Code for model training
- B. Partial dependence plots (PDPs) Correct
- C. Sample data for training
- D. Model convergence tables
Explanation
Partial Dependence Plots (PDPs) are a valuable tool for explaining the relationship between features and predictions in machine learning models, providing transparency into how each feature affects the outcome. This aligns with AWS's emphasis on model explainability to ensure stakeholders understand model behavior.
