ML Functional Performance refers to how well a machine learning (ML) model meets predefined functional criteria during evaluation and operation. It assesses the model’s effectiveness in performing its intended tasks.
Key Aspects:
- Accuracy: Measures how often predictions match actual outcomes.
- Robustness: Evaluates performance under varying conditions or datasets.
- Efficiency: Assesses resource usage during model execution.
Monitoring ML functional performance ensures that models deliver reliable results in real-world applications.