Detecting Data Anomalies
Investigate outliers, rare events, spikes, and suspicious records in datasets. Use as an explicit anomaly-analysis helper when you want concrete anomaly-detection workflow guidance, not generic data validation or end-to-end ML ownership.
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# 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
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