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 ?
Insights: Tree-based, Coefficients-based, Word clouds, etc.
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.