I referred to 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
c as my features, I want to know what is the effect of
ca towards the prediction result. It is very similar to
lime, but for combinations of features.