Selective Classification

Selective classification enhances machine learning models by allowing them to abstain from predictions when confidence is low, improving reliability, especially in high-stakes applications. Current research focuses on developing robust evaluation metrics, improving confidence estimation through techniques like contrastive learning and logit normalization, and addressing challenges like distribution shifts and out-of-distribution data. This field is significant because it bridges the gap between theoretical model performance and real-world deployment, leading to more trustworthy and reliable AI systems across various domains.