# Error Handling Reference

Common issues and solutions:

**Insufficient Data Volume**
- Error: Not enough data points for statistical significance
- Solution: Collect more data, adjust contamination rate, or use simpler statistical methods

**High False Positive Rate**
- Error: Too many normal points classified as anomalies
- Solution: Adjust detection threshold, refine feature selection, or use domain-specific constraints

**Algorithm Performance Issues**
- Error: Detection algorithm too slow for large datasets
- Solution: Use sampling techniques, optimize parameters, or switch to faster algorithms like Isolation Forest

**Feature Scaling Problems**
- Error: Anomalies dominated by high-magnitude features
- Solution: Apply appropriate normalization or standardization to all features before detection

**Missing Ground Truth**
- Error: Unable to validate detection accuracy without labels
- Solution: Use domain expertise for manual validation, implement feedback loop for model improvement