Amazon Exam AIF-C01 Topic 1 Question 25 Discussion

Partial dependence plots (PDPs) are visual tools used to show the relationship between a feature (or a set of features) in the data and the predicted outcome of a machine learning model. They are highly effective for providing transparency and explainability of the model's behavior to stakeholders by illustrating how different input variables impact the model's predictions.

Option B (Correct): 'Partial dependence plots (PDPs)': This is the correct answer because PDPs help to interpret how the model's predictions change with varying values of input features, providing stakeholders with a clearer understanding of the model's decision-making process.

Option A: 'Code for model training' is incorrect because providing the raw code for model training may not offer transparency or explainability to non-technical stakeholders.

Option C: 'Sample data for training' is incorrect as sample data alone does not explain how the model works or its decision-making process.

Option D: 'Model convergence tables' is incorrect. While convergence tables can show the training process, they do not provide insights into how input features affect the model's predictions.

AWS AI Practitioner Reference:

Explainability in AWS Machine Learning: AWS provides various tools for model explainability, such as Amazon SageMaker Clarify, which includes PDPs to help explain the impact of different features on the model's predictions.