Technical GlossaryData Science and Data Management

One-Class Classification

A modeling approach that learns the normal pattern and treats deviations as anomalous when the minority class is extremely rare.

One-class classification can be used instead of standard binary classification when minority-class examples are extremely scarce. The model learns the boundary of normal or dominant behavior, and examples falling outside that boundary are treated as anomalies or suspicious cases. It is useful in cybersecurity, fraud detection, equipment failure, and rare medical event scenarios. This approach reframes the imbalance problem not as direct class prediction, but as modeling normality and detecting deviations from it.