Reject Curve

Reject curves are visualization tools used to assess the trade-off between rejecting uncertain classifications and achieving high classification performance in machine learning models. Recent research emphasizes improving the interpretability of these curves, particularly for non-experts, and exploring alternative performance metrics beyond accuracy, such as precision and recall, especially for imbalanced datasets. This work is crucial for applications where reliable classification is paramount, such as healthcare and autonomous systems, enabling better model selection and more informed decision-making.