Feature importance over a subset of instance space instead of an entire instance space
This is a common occurrence in practice. For example, in the bioinformatics space, a protein can change a phenotype significantly over a very limited range, but outside that range has little to no effect.

If you want to where the 'range of highest effect' falls I recommend partial dependence plots which are means of ICE plots. It offers an advantage over decision trees in that you won't overfit as easily to your training dataset and can be applied to high dimensional data. Here's an example of the plot. Read more here. The following packages have some implementation of the above:


mlinsights - https://ml-insights.readthedocs.io/en/latest/
https://media.readthedocs.org/pdf/pdpbox/latest/pdpbox.pdf


Here's a decent example from the above:


If you only want to know if something is important, not where it is important. You can use something called permutation importance or Mean Decrease in Accuracy outlined very briefly in a seminal random forest paper, page 20-21: https://www.stat.berkeley.edu/~breiman/randomforest2001.pdf

Look into the eli5 package for easy calculation.