ML Functional Performance Metrics are measures that assess the correctness of machine learning (ML) systems. These metrics evaluate how well an ML model performs its intended tasks, such as making predictions or classifications. They help ensure that the model meets specified functional requirements.
Key Aspects:
- Accuracy: The proportion of correct predictions made by the model.
- Precision: The ratio of true positive predictions to the total predicted positives.
- Recall: The ratio of true positive predictions to the actual positives.
- F1 Score: The harmonic mean of precision and recall, providing a balance between the two.
- AUC-ROC: The area under the receiver operating characteristic curve, indicating the model’s ability to distinguish between classes.
These metrics are essential for validating ML models in real-world applications. They help identify areas for improvement and ensure that models operate effectively in their intended environments.