Design an anomaly detection system with the right algorithm, threshold strategy, and evaluation for rare, unlabeled outliers.
## CONTEXT Anomaly detection underpins fraud, fault, intrusion, and quality monitoring, and it is uniquely hard because anomalies are rare, often unlabeled, and constantly evolving. In 2026 the methods span statistical (z-score, robust covariance), distance and density (Isolation Forest, Local Outlier Factor), and…
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