Editing Anomaly Detection (section)

== <span style="color: #FFFFFF;">Creating</span> ==
Designing a production anomaly detection pipeline:
# Data collection: identify all relevant signals for the domain (sensor readings, transaction features, log events).
# Baseline modeling: fit Isolation Forest on 30 days of normal data.
# Threshold setting: use 99th percentile of normal data scores as initial threshold.
# Monitoring: track alert rate daily; alert if rate changes >3× (suggests concept drift or system issue).
# Continuous learning: retrain model monthly on sliding window of data.
# Human-in-the-loop: all alerts reviewed by analyst; verdicts feed back into labeled dataset for supervised upgrade.
# Alert deduplication: suppress repeated alerts for the same entity within a time window to reduce fatigue.

[[Category:Artificial Intelligence]]
[[Category:Machine Learning]]
[[Category:Anomaly Detection]]
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