How do you evaluate an unsupervised anomaly detection model?
Updated May 5, 2026
Short answer
By using a small labeled validation set or internal metrics[cite: 1].
Deep explanation
Even if the model is trained unsupervised, you need a gold-standard labeled set to calculate Precision, Recall, and AUC-ROC[cite: 1].
Real-world example
Benchmarking fraud systems against historical 'chargeback' labels[cite: 1].
Common mistakes
- Relying on accuracy in highly imbalanced datasets[cite: 1].
Follow-up questions
- What is the most important metric?