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!!