# difference between logistic regression and binary logistic regression

In xgboost R package, there are two objectives given with booster gbtree.

1. reg:logistic
2. binary:logistic

See,page 22 (first 2 lines) https://cran.r-project.org/web/packages/xgboost/xgboost.pdf

I was wondering, what is the difference between these two methods?

• – Ben Reiniger Aug 14 '19 at 18:15

## 1 Answer

binary:logistic is used for binary classification where the target variable takes binary output [0, 1]

reg:logistic is used for regression where the target variable is continuous between [0, 1]

Quote from xgboost doc:

We use linear regression here, if we want use objective = reg:logistic logistic regression, the label needed to be pre-scaled into [0,1]

https://github.com/dmlc/xgboost/tree/master/demo/regression

• Would be nice if you could add the objective in its mathematical form – Martin Thoma Apr 16 '18 at 13:06