Definition
Evaluation involves systematically measuring how well an AI model performs on specific tasks using quantitative metrics (e.g., accuracy, F1-score, BLEU) and qualitative assessments. It includes validation, testing, and stress-testing to ensure reliability. Effective evaluation identifies biases, overfitting, and generalization errors, providing essential feedback for iterative model improvement and ensuring safety before deployment in real-world scenarios.
Summary
Evaluation is the process of assessing the performance, accuracy, and robustness of an AI model against predefined metrics and datasets.
Key Concepts
- Metrics
- Validation Set
- Generalization
- Benchmarking
Use Cases
- Comparing model versions during hyperparameter tuning
- Auditing models for fairness and bias
- Certifying AI systems for regulatory compliance