Bayes Error

Bayes error represents the fundamental limit of classification accuracy achievable by any model, determined by the inherent uncertainty in the data. Current research focuses on estimating this limit, particularly for deep neural networks, employing techniques like approximate message passing and analyzing the impact of model architecture (e.g., extensive-width networks) and regularization strategies. Understanding Bayes error is crucial for evaluating the performance of state-of-the-art models, identifying potential overfitting, and establishing realistic expectations for achievable accuracy in various applications, including robust machine learning and scientific decision-making. This informs the development of more efficient and effective machine learning algorithms.