How do you evaluate anomaly detection metrics?

2 ROC and AUC

Another way to evaluate anomaly detection models is to use the receiver operating characteristic (ROC) curve and the area under the curve (AUC). The ROC curve plots the true positive rate (TPR) versus the false positive rate (FPR) for different threshold values of the model's output. The AUC measures the overall performance of the model across all possible thresholds, with a higher value indicating a better model. The ROC curve and the AUC are useful for comparing different models or tuning the optimal threshold for a given model. However, they may not be very sensitive to rare anomalies or imbalanced data, where the number of normal instances is much larger than the number of anomalous instances.

3 Precision-recall curve and AP

A third way to evaluate anomaly detection models is to use the precision-recall curve and the average precision (AP). The precision-recall curve plots the precision versus the recall for different threshold values of the model's output. The AP measures the average precision across all possible thresholds, with a higher value indicating a better model. The precision-recall curve and the AP are more suitable for anomaly detection problems with rare anomalies or imbalanced data, as they focus more on the positive class (anomalies) than the negative class (normal instances). However, they may not reflect the overall accuracy or specificity of the model, as they do not account for the true negatives or false negatives.

4 Novelty and outlier scores

A fourth way to evaluate anomaly detection models is to use the novelty and outlier scores, which are based on the model's ability to learn from normal data and generalize to new data. The novelty score measures how well the model can detect anomalies in new data that was not used for training, while the outlier score measures how well the model can detect anomalies in the same data that was used for training. A high novelty score means that the model is robust and adaptable, while a high outlier score means that the model is sensitive and consistent. However, novelty and outlier scores may not be comparable across different models or datasets, as they depend on the model's assumptions and parameters.

5 Domain-specific metrics

A fifth way to evaluate anomaly detection models is to use domain-specific metrics, which are tailored to the specific problem or application that you are trying to solve. For example, if you are using anomaly detection for fraud detection, you may want to use metrics such as the cost of fraud, the return on investment, or the customer satisfaction. If you are using anomaly detection for system monitoring, you may want to use metrics such as the downtime, the availability, or the reliability. Domain-specific metrics can help you measure the real-world impact and value of your anomaly detection models, but they may not be standardized or generalizable across different domains or scenarios.