Model Coverage refers to the extent to which elements within a machine learning model are tested or evaluated during the testing process. It ensures that all aspects of a model’s functionality are assessed for quality and performance.
Key Components:
- Feature Coverage: Ensures all features used by the model are tested.
- Data Coverage: Verifies that various data inputs are considered during testing.
- Path Coverage: Checks all possible execution paths within the model’s logic.
High model coverage is essential for identifying potential issues and ensuring robust performance across diverse scenarios.