LIME ML Interpreter mode Classification or Regression for Isolation Forest (Anomaly Detection)
For us, what we have done is as follows:

Use Isolation Forest to get anomalies.
Treat 1 and -1 returned by Isolation Forest as class labels and build a Random Forest classifier.
Pass this Random Forest classifier to LIME to get explanation of anomalous points.

We are also trying to find a better option instead of building second level Random Forest classifier.
i am trying to find anomalies in my dataset of 1000+ documents. I'm using LIME ML Interpreter to be able to explain the model (Isolation Forest) predictions. In one parameter "mode" i am able to choose between Classification and Regression. I do not have a set of documents with a known anomaly.
Since Isolation Forest is a unsupervised learning method and classifcation is a type of supervised learning which is used to clasify observations into two or more classses i ended up using regression. On the other side i have the outcome anomaly or no anomaly.
What is right to use here?
Best Regards,
Elle