Anomaly Detection

Summary

Identifying data points that don't fit the normal pattern — used for fraud detection, equipment failure prediction, and data quality monitoring, often building on clustering or density-based methods.

• 1Try a simple anomaly detection method (e.g. Isolation Forest) on a sample dataset.
• 2Learn why anomaly detection is often framed as an unsupervised problem (labeled anomalies are rare).
• 3Understand the trade-off between catching more anomalies and generating more false alarms.