juniorAnomaly Detection
Explain the difference between Supervised and Unsupervised Anomaly Detection.
Updated May 5, 2026
Short answer
Supervised uses labeled data; unsupervised finds patterns in unlabeled data[cite: 1].
Deep explanation
Supervised models are trained on known examples of 'normal' and 'anomaly'. Unsupervised models assume anomalies are rare and located in low-density regions[cite: 1].
Real-world example
Unsupervised is used for detecting new types of zero-day cyber attacks[cite: 1].
Common mistakes
- Using supervised learning when the 'anomaly' class is too small to learn from[cite: 1].
Follow-up questions
- What is semi-supervised detection?