There are plenty of working for explaining prediction in supervised learning (e.g. SHAP values, LIME).
What about for anomaly detection in unsupervised learning? Is there any model for which there are libraries that can give you justifications, such as "row x is an anomaly because feature 1 is higher than 5.3 and feature 5 is equal to 'No'"?
Topic explainable-ai anomaly-detection outlier
Category Data Science
There is a library called outliertree which is doing exactly that. It uses decision trees to provide human readable explanations of outliers/anomalies.
Also see the related paper Explainable outlier detection through decision tree conditioning.
you can use SHAP Kernal Explainer for unsupervised model but you should get sth as output. you might need to create a pipeline so that you just pass pipeline variable to shap kernal.
The LIME framework can probably be used to do this as well.
Outlier detection sets a specific label to outliers (say 1), and another one to inliers (say 0). From then on, you can train interpretable models (decision trees for instance) to predict the labels set by your unsupervised model.
I don't know much about SHAP values, but I guess, with this approach, you could do the same.
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