Evaluation Framework

Evaluation frameworks are crucial for assessing the performance and reliability of various machine learning models and systems, particularly in high-stakes applications like healthcare and security. Current research emphasizes developing standardized, comprehensive frameworks that address specific challenges, such as data imbalance, domain mismatch, and the need for interpretability and explainability. These frameworks often incorporate novel metrics beyond traditional accuracy measures, focusing on aspects like fairness, robustness, and alignment with real-world application needs. The resulting improvements in evaluation methodologies will enhance the development and deployment of more reliable and trustworthy AI systems across diverse fields.