1. fit distribution model $p(x)$ on training set and predict
   $y=1$ if $p(x)<ϵ$ (anomaly)
   $y=0$ if $p(x)>=ϵ$ (normal)
2. evaluate the algorithm using
   • true positive, false positive...
   • precision/recall
   • F1-score: Confusion Matrix#F1-score

Anomaly detection vs. supervised learning

Why bother using anomaly detection rather than supervised learning if your examples already have labels?

• Anomaly detection is more proper than supervised learning, when:
  • very small number of anomalous examples
  • future anomalies may look nothing like any of the anomalous examples you've seen so far
• Examples of application
  • anomaly detection: fraud detection
  • supervised learning: spam emails