Scoring Function

Scoring functions are mathematical formulas used to evaluate the quality or likelihood of different outcomes, crucial in diverse fields from machine learning to actuarial science. Current research focuses on developing more efficient and robust scoring functions, often leveraging machine learning techniques like graph neural networks and adversarial reinforcement learning to optimize their performance and address issues like bias and overconfidence. Improved scoring functions are vital for enhancing the accuracy and efficiency of various applications, including molecular docking, credit scoring, and model selection in mixed-integer programming, ultimately leading to better decision-making across numerous domains.