In an unsupervised setting for higher-dimensional data (e.g. 10 variables (numerical and categorical), 5000 samples, ratio of anomalies likely 1% or below but unknown) I am able to fit the isolation forest and retrieve computed anomaly scores (following the original paper and using the implementation in scikit-learn). This gives me a ranking of potential anomalies to consider. However, how would I further assess the validity of these flags? How can I understand which feature has contributed to the anomaly score the most? Feature importance techniques usually applied in random forests do not seem to work in case of the isolation forest.
Interested to hear your thoughts. Any help is very appreciated.