Isolation Forest is widely used when dealing with outlier/anomaly detection when we have no labels. The theory behind is that making random split at random points and counting how many splits you do to isolate a feature will help you determine if an instance is or not an outlier. I have categorical features and I am not sure how to deal with them: Label Encoding: Will misrepresent the data in euclidean space. One Hot Encoding: Will give me more...
Originally asked by Carlos Mougan on Stack Exchange (datascience) (CC BY-SA 4.0).
Variant 76.
Variant 181.