In short
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.
How to learn this
- 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.
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