Hi @data-dino , the feature importance method we use is called "Alternating Conditional Expectation (ACE)". It is a univariate measure of correlation between the feature and the target. ACE scores detect non-linear relationships, but as they are univariate, they do not detect interaction effects. If you want to go into more details you can have a look to this paper: https://www.jds-online.com/files/JDS-156.pdf
You can't choose a different method. Out of curiosity, which method you have in mind ?
Datarobot provides a variety of insights for features that may be addressed as feature importance:
Overall, DataRobot recommends using either permutation-based or SHAP-based Feature Impact as they show results for original features in predictive models.
Hope this answer will help. If you need further explanations - feel free to ask.
Hi @data-dino, I'd like to ask a clarifying question. What are you planning on doing once you look at feature importance? What decision will you make based on what you discover?
If I understand that I can help recommend what might be best for you.