When you train your XGBoost regression model, you can obtain feature importances by using:
model.get_score(importance_type="gain")
Although I tried to reconstruct the value and have done some research on it, I am still struggling to figure out, how gain is computed in XGBoost?
It is partially explained here: Relative variable importance for Boosting
But this is focused on classification. And even though there is a link saying that squared error with Friedman's improvement score
is used, I have not reached the same numbers.