I referred to [this][1] beautiful document to research about joint feature contibutions. But this works only for RandomForest algorithms because of `treeinterpreter` (does not work with `xgboost`). Is there a similar way out for XGBoost as well?

Basically what I want to achieve is to find out the joint contributions of all the combination of features towards the prediction. For example if I have `a`, `b` and `c` as my features, I want to know what is the effect of `ab`, `bc` and `ca` towards the prediction result. It is very similar to `shap` and `lime`, but for combinations of features.


  [1]: http://savvastjortjoglou.com/intrepretable-machine-learning-nfl-combine.html#Joint-Feature-Contributions