The package is great; but I got a non-sense issue. I have a xgboost binary classification model (objective = "binary:logistic", metric = "auc) with roughly 1000 trees and 200 variables. When I am trying to breakdowm the model for a new observation using "broken", it takes 1.5 hour per each observation!! Is it normal or something is wrong?
Should I specify the prediction function? DMatrix or model.matrix objects could be source of the problem?
In this implementation, can I use DMatrix object without using "model.matrix"? If so how?
Hi Biecek,
The package is great; but I got a non-sense issue. I have a xgboost binary classification model (objective = "binary:logistic", metric = "auc) with roughly 1000 trees and 200 variables. When I am trying to breakdowm the model for a new observation using "broken", it takes 1.5 hour per each observation!! Is it normal or something is wrong?
Should I specify the prediction function? DMatrix or model.matrix objects could be source of the problem? In this implementation, can I use DMatrix object without using "model.matrix"? If so how?
Thanks,
Amir