conditional subgroup feature importance about flashlight HOT 2 CLOSED

There is no theoretic definition of "feature importance", so there is no "best" way. But maybe some methods might produce more informative results than others in your situation.

If you would know the groups, then you could:

1. Use the permutation importance function in Christoph's "iml" package. There, you can pass a list of feature groups that should be permuted together. This is a feature that would be nice to have in "flashlight".
2. Calculate SHAP importance of feature groups (row-wise summing up the SHAP values of each feature group, then take the mean absolute value per column). This has the disadvantages being an in-sample measure of importance, while 1. would produce an out-of-sample result if done on an independent data set.

If you do not know the groups, as an alternative to Christoph's approach, I think you could use a variable clustering algorithm to build the groups and then use any of above methods.

from flashlight.

Thanks @mayer79
I think I now know how to argue this problem. Appears there is no clean solution to this problem and requires a secondary analysis.

Thanks a lot for enlightening me.

from flashlight.