How do I evaluate outlier detection models?

How do I evaluate outlier detection models?

To evaluate outlier detection models, you can use metrics like precision, recall, F1-score, and ROC-AUC.

What is the importance of precision and recall in this context?

Precision measures the accuracy of the outlier predictions, while recall indicates how many actual outliers were detected.

Can you explain ROC-AUC and how it applies to outlier detection?

ROC-AUC evaluates the trade-off between true positive rates and false positive rates, helping to assess model performance.

Are there any specific datasets you recommend for testing these models?

Yes, the KDD Cup 1999 dataset and the Credit Card Fraud Detection dataset are popular for testing outlier detection models.