Model Benchmark

Model benchmarking aims to objectively evaluate the performance of machine learning models, focusing on accuracy, bias, and efficiency across diverse tasks and datasets. Current research emphasizes developing more precise and nuanced evaluation metrics, particularly for addressing model biases and ensuring local validity, and explores the use of active learning and empirical Bayes methods to improve efficiency. These advancements are crucial for building trustworthy and reliable models, impacting various fields from manufacturing and official statistics to computer vision and natural language processing.