Decision Threshold

Decision thresholds are critical parameters in machine learning models, determining the classification boundary between different outcomes. Current research focuses on improving the reliability and fairness of these thresholds, particularly in high-stakes applications like medical diagnosis and autonomous systems, often employing techniques like misclassification likelihood matrices and optimal transport algorithms to optimize threshold selection. This work aims to enhance model interpretability, mitigate risks associated with misclassifications, and ensure equitable outcomes across different demographic groups, ultimately leading to more robust and trustworthy AI systems.