0
$\begingroup$

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'"?

$\endgroup$
  • $\begingroup$ SHAP values and the shap Python library can be used, see this answer. $\endgroup$ – jonnor Jun 14 at 11:43
2
$\begingroup$

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.

| improve this answer | |
$\endgroup$
0
$\begingroup$

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.

| improve this answer | |
$\endgroup$

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.