Anomaly Detection

Anomaly detection is a binary classification task aimed at identifying unusual or unexpected patterns that significantly deviate from the majority of data in a dataset. The goal of this task is to discover these outliers, which may represent errors, fraud, or other types of abnormal events, and mark them for further investigation. Anomaly detection has significant application value in areas such as financial risk control, cybersecurity, and medical diagnosis.