Performance Metric

Performance metrics are crucial for evaluating the effectiveness of machine learning models, particularly in complex applications like recommender systems, code generation, and medical image analysis. Current research emphasizes aligning automated metrics with human preferences, developing causal frameworks for auditing model behavior, and addressing challenges like imbalanced datasets and the need for metrics sensitive to different error types. This work is vital for ensuring model reliability, fairness, and ultimately, the responsible deployment of AI across diverse fields.