How do I evaluate different anomaly detection methods?

How do I evaluate different anomaly detection methods?

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

What datasets are best for testing these methods?

Common datasets include the KDD Cup 1999, the UNSW-NB15, and the CICIDS datasets, which are widely used for benchmarking.

Can you explain precision and recall in this context?

Sure! Precision measures the accuracy of positive predictions, while recall measures the ability to find all relevant instances.

Are there any specific tools for evaluating these methods?

Yes, tools like Scikit-learn, PyOD, and TensorFlow can help you implement and evaluate various anomaly detection algorithms.