Area Under The Curve (AUC)

class dtaianomaly.evaluation.AreaUnderPR

Computes the Area Under the Precision-Recall Curve (AUC-PR) score.

The AUC-PR is a performance metric that is especially useful for evaluating models in imbalanced datasets, such as anomaly detection, where the number of normal instances vastly outnumbers the anomalies. The Precision-Recall curve plots precision against recall at various thresholds, providing a detailed view of the trade-off between detecting true anomalies (recall) and minimizing false alarms (precision).

AUC-PR summarizes the curve into a single value, representing the overall ability of the model to identify anomalies while keeping false positives in check. A higher AUC-PR value indicates better performance, meaning the model is effective at detecting true anomalies with fewer false positives.

class dtaianomaly.evaluation.AreaUnderROC

Computes the Area Under the Receiver Operating Characteristic Curve (AUC-ROC) score.

The AUC-ROC is a widely used metric to evaluate the performance of a binary classifier, especially in anomaly detection. The ROC-curve plots the true positive rate (recall) against the false positive rate across different classification thresholds. The AUC-ROC represents the likelihood that the model ranks a randomly chosen anomaly higher than a randomly chosen normal instance.

AUC-ROC provides a single number summarizing the model’s ability to distinguish between normal and anomalous instances. A value of 1.0 indicates perfect discrimination, while 0.5 implies the model performs no better than random guessing. It is especially useful when anomalies are rare, as it considers the trade-off between detecting true anomalies (high recall) and minimizing false positives.