12. Statistical and Machine-Learning Detection

Optimize calibrated decisions and ecosystem outcomes, not leaderboard accuracy.

Reader outcome

Choose and evaluate classification, anomaly detection, clustering, and supervised or unsupervised methods against base rates, costs, and operational constraints.

Decision questions

• Where do labels come from and which enforcement bias do they encode?
• Is probability calibrated for this population and current time?
• What precision is achievable at the real base rate?
• How will drift, feedback loops, evasion, and delayed labels be detected?

Evidence needed

• Precision-recall and calibration studies
• Offline-to-online failure cases
• Model governance and feature provenance
• Cost-sensitive and selective-classification methods