I referred to Joint Feature Contributions this 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 combination of features.