Anomaly Detection

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Anomaly detection helps identify data that doesn’t fit expected behavior, for example, spotting fraudulent transactions or detecting equipment failures. These rare or unusual cases often signal important issues that need attention. To find them, AI models learn what “normal” looks like and then flag anything that stands out. Depending on the problem, this can be done with labeled data (supervised), partially labeled data (semi-supervised), or without labels (unsupervised).

Common techniques include statistical analysis, clustering, and machine learning methods like isolation forests or neural networks. Because anomalies are rare, measuring performance focuses on how many real anomalies the system catches without raising too many false alarms.

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