Instance Score

Instance scores quantify the influence of individual data points on a model's prediction, aiding in model explainability and improving model performance. Current research focuses on developing robust and efficient methods for calculating instance scores, particularly using Shapley values and their approximations, and exploring their applications in diverse areas like data selection, outlier detection, and improving model generalization. These advancements enhance the interpretability of complex models and contribute to more reliable and trustworthy machine learning systems across various domains.