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detecting-data-anomalies

separate

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.

When to use

• 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

When not to use

• Null validation
• Duplicate validation
• Schema validation
• Range validation
• Full model training
• End-to-end pipeline ownership
• Publication-grade figure production

Skill page

Source