Anomaly detection models are used to identify outliers, or unusual instances, in the data. Unlike other modeling methods that store rules about unusual cases, anomaly detection models store information on what normal behavior looks like. This makes it possible to identify outliers even if they do not conform to any known pattern, and it can be particularly useful in applications where new patterns may constantly be emerging. Anomaly detection is an unsupervised method, which means that it does not require a training dataset containing known anomalies to use as a starting point.
- For more information about this node, see Anomaly Overview.
- For more information about the visualizations for this node, see Anomaly Visualizations.
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