Handling Imbalanced Classes in supervised detection.

Use SMOTE, cost-sensitive learning, or Precision-Recall metrics[cite: 1].

Since anomalies are rare, accuracy is a misleading metric. Use F1-score or AUPRC. Penalize the model more for missing an anomaly than for a false alarm[cite: 1].