Multi-class Classification

5 benchmarks903 papers

Multi-class classification is a type of supervised learning where the goal is to assign an input to one of three or more distinct classes. Unlike binary classification (which has only two classes), multi-class classification handles multiple labels and uses algorithms like logistic regression, decision trees, random forests, SVMs, or neural networks to predict the correct category based on the features of the input data.

Benchmarks

Multi-class Classification on Reuters-52

Macro F1

Multi-class Classification on COVID chest X-ray

F1 score

Multi-class Classification on COVID-19 CXR Dataset

Accuracy (%)

Multi-class Classification on TII-SSRC-23

F1-Score

Multi-class Classification on Training and validation dataset of capsule vision 2024 challenge.

Mean AUC