anomaly-detection

Anomaly detection is a critical process in machine learning that involves identifying rare items, events, or observations that raise suspicions by differing significantly from the majority of the data. Misconceptions about anomaly detection often include the belief that it is only applicable to fraud detection, while it also applies to network security, fault detection, and quality control. Anomaly detection methods can be broadly categorized into supervised and unsupervised techniques, each with its own set of challenges and trade-offs.