detecting-data-anomalies

# Detecting Data Anomalies ## Positioning Treat this skill as an explicit/manual helper. In governed ML routing, anomaly-detection ownership normally belongs to `scikit-learn`. ## When to Use Use this skill when: - Reviewing outlier transactions, fraud candidates, sensor spikes, or rare failures - Comparing isolation forest, one-class SVM, LOF, or threshold-based anomaly workflows - Turning suspicious records into a shortlist for human inspection ## Not For / Boundaries - Null/duplicate/schema/range validation: use `exploratory-data-analysis` - Full model training or end-to-end pipeline ownership: use `scikit-learn` or `ml-pipeline-workflow` - Publication-grade figure production: use `scientific-visualization` ## Typical Outputs - Candidate anomaly-detection methods and thresholds - A review checklist for false positives and false negatives - Suggested tables or plots for the suspicious subset ## Related Skills - `scikit-learn` as the governed routed owner for classical anomaly-detection workflows - `creating-data-visualizations` after anomalies are identified