Multiclass Classification

Multiclass classification studies how to assign an input to one of several possible categories, a task used in settings from medical diagnosis and image recognition to text analysis and fault detection. Common approaches include softmax and linear classifiers, decision trees and boosting, neural networks, ensembles, and structured methods such as one-vs-rest or hierarchical classification. Researchers also develop losses and sampling strategies for noisy or imbalanced data, and use classwise recall, macro-F1, calibration, and prediction sets to assess reliability beyond overall accuracy. A central challenge is balancing strong average performance with dependable predictions for rare or ambiguous classes; theoretical work studies sample-complexity limits, while practical work seeks methods that remain efficient, fair, and robust.