Anomaly Detection
Anomaly detection identifies unusual patterns in data to detect failures, fraud, or security incidents early. The concept covers statistical techniques, rule-based systems and machine learning, including operations, evaluation and adaptation to concept drift. Deployment requires data preparation, model validation and continuous monitoring. Trade-offs include sensitivity, false-positive rate and compute costs.
This block bundles baseline information, context, and relations as a neutral reference in the model.
Definition · Framing · Trade-offs · Examples
What is this view?
This page provides a neutral starting point with core facts, structure context, and immediate relations—independent of learning or decision paths.
Baseline data
Context in the model
Structural placement
Where this block lives in the structure.
No structure path available.
Relations
Connected blocks
Directly linked content elements.