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Getting Started with Amazon SageMaker Studio
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SageMaker Clarify also computes model-agnostic feature attribution based on the concept of Shapley values. Shapley values can be used to determine the contribution each feature makes to model predictions. Feature attribution helps explain how a model makes decisions. Having a quantifiable approach to describe how a model makes decisions enables us to have trust in an ML model that meets regulatory requirements and supports the human decision-making process.
Similar to setting up configurations to run bias analysis jobs using SageMaker Clarify, it takes three configurations to set up a model explainability job: a data configuration, a model configuration, and an explainability configuration. Let's follow the next steps from the same notebook:
explainability_data_config...