5.16 Anomaly detection
You can flag rare events without a labelled dataset.
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Anomaly detection flags rare events without labelled examples — statistical thresholds, Isolation Forest, autoencoder reconstruction error — and it powers fraud screens, fault detection and monitoring. It sits here because it composes the unsupervised ideas into a deployable job. The hard part is evaluation: with almost no true positives, accuracy is meaningless, precision is fragile, and knowing which numbers survive rarity is half the skill.
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Statistical thresholds and robust z-scores
Simple thresholds on how far a value sits from typical, using measures that are not themselves distorted by the outliers. It is the baseline every more elaborate method must beat.
Isolation Forest and One-Class SVM
Two methods designed for the unlabelled case, one isolating unusual points by how quickly they can be separated, the other learning a boundary around normal data. Both are standard first choices.
Autoencoder reconstruction error
Training a network to reconstruct normal data and flagging what it reconstructs badly. It suits high-dimensional data where simpler distances stop working.
Evaluating detectors when positives are rare
With very few positives, ordinary metrics become meaningless and evaluation needs care about what a false alarm actually costs. This is where most anomaly projects go wrong.
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