Hi,

I have trained xgboost model in spark with one tree model with “-booster gbtree --eval_metric ndcg --objective rank:pairwise”, the dumped model text is as shown below.

booster[0]:

0:[feature1<2323] yes=1,no=2,missing=2

1:[feature2<2.00000095367431640625] yes=3,no=4,missing=4

3:leaf=0.1649394333362579345703125

4:leaf=0.049700520932674407958984375

2:[feature2<2.00000095367431640625] yes=5,no=6,missing=6

5:leaf=0.0433560945093631744384765625

6:leaf=-0.09195549786090850830078125

The test data with only one record

feature1: 511

feature2: missing

It suppose to routed to leaf4: **0.049700520932674407958984375**. However, the model predicting score gives **0.5497005**. Is there an internal transfer function from the leaf score to the final predicting score inside xgboost? Can someone point a link? Thank you very much!!