Classification

Classification studies how to assign inputs such as images, text, signals, graphs, and tabular records to discrete categories, supporting decisions across science and everyday systems. Methods range from linear and kernel discriminants, trees and ensembles, and deep neural networks to pretrained language and vision models, using supervised, few-shot, semi-supervised, and in-context learning. Current work also addresses class imbalance, label noise, distribution shift, open-set recognition, calibration, abstention, and structured labels through robust losses, uncertainty estimates, retrieval, and hierarchical or multimodal models. A central tension is that high benchmark accuracy can conceal shortcut reliance, poor calibration, subgroup failures, or weak transfer, so evaluation increasingly tests reliability, error costs, and performance under deployment shifts.