Is there a bug of hinge loss for binary classification(binary:hinge)?


#1

I usebinary:hinge as my objective function when training a binary classification task. The max_depth was set to 1. And I found the metrics(auc, logloss, error) keep the same during training process. Like

[509]	train-auc:0.5	train-logloss:10.6287	train-error:0.288653
[510]	train-auc:0.5	train-logloss:10.6287	train-error:0.288653
[511]	train-auc:0.5	train-logloss:10.6287	train-error:0.288653
[512]	train-auc:0.5	train-logloss:10.6287	train-error:0.288653
[513]	train-auc:0.5	train-logloss:10.6287	train-error:0.288653
[514]	train-auc:0.5	train-logloss:10.6287	train-error:0.288653
[515]	train-auc:0.5	train-logloss:10.6287	train-error:0.288653
[516]	train-auc:0.5	train-logloss:10.6287	train-error:0.288653
[517]	train-auc:0.5	train-logloss:10.6287	train-error:0.288653
[518]	train-auc:0.5	train-logloss:10.6287	train-error:0.288653
[519]	train-auc:0.5	train-logloss:10.6287	train-error:0.288653
[520]	train-auc:0.5	train-logloss:10.6287	train-error:0.288653
[521]	train-auc:0.5	train-logloss:10.6287	train-error:0.288653
[522]	train-auc:0.5	train-logloss:10.6287	train-error:0.288653
[523]	train-auc:0.5	train-logloss:10.6287	train-error:0.288653

I dumped the model in the end, and I found that there are many trees with the same structure and selected feature. And some trees has only one node. Such as:
image

Is there a bug of hinge loss for binary classification(binary:hinge)? or Can some one give me some advice about the usage of hinge loss in xgboost.