Each quarter, a company uses ML models to forecast demand and wants to produce a report that gives stakeholders clear explanations of how the models make predictions. Which item should the AI practitioner include in the report to provide model transparency and explainability?

Partial dependence plots (PDPs) are effective for model transparency and explainability because they show the marginal effect of one or two features on the predicted outcome of a machine learning model. This allows stakeholders to understand how specific input variables influence the model's predictions, providing clear insights into its behavior. The code used to train the model is too technical for most stakeholders and doesn't directly explain predictions. Samples of training data don't explain how the model uses that data to make predictions. Tables showing model convergence indicate training success but don't explain the model's decision-making process for specific predictions.