What are the differences between supervised and unsupervised outlier detection?

What are the differences between supervised and unsupervised outlier detection?

Supervised outlier detection uses labeled data to train models, while unsupervised methods identify anomalies without labels.

Can you give examples of each method?

Sure! Supervised methods include decision trees and SVMs, while unsupervised methods include clustering algorithms like DBSCAN.

Which method is generally more effective?

It depends on the context. Supervised methods can be more accurate with labeled data, but unsupervised methods are more flexible.

What are the challenges of using each method?

Supervised methods require labeled data, which can be hard to obtain, while unsupervised methods may produce false positives.