Quantitative Evaluation

Quantitative evaluation in machine learning focuses on developing and applying objective metrics to assess the performance, reliability, and explainability of models across diverse applications. Current research emphasizes the development of novel metrics tailored to specific domains, such as assessing the reliability of medical image classifiers or evaluating the fairness of language models, often employing techniques like adversarial training and information-theoretic measures. This rigorous approach is crucial for building trust in AI systems and ensuring their responsible deployment in various fields, from healthcare and weather forecasting to autonomous vehicles and 3D modeling.