I use the below params to train.
XGBoosterSetParam(h_booster, "booster", "gbtree");
XGBoosterSetParam(h_booster, "objective", "binary:logistic");
XGBoosterSetParam(h_booster, "max_depth", "5");
XGBoosterSetParam(h_booster, "eta", "0.1");
XGBoosterSetParam(h_booster, "silent", "1");
XGBoosterSetParam(h_booster, "min_child_weight", "1");
XGBoosterSetParam(h_booster, "subsample", "0.5");
XGBoosterSetParam(h_booster, "colsample_bytree", "1");
XGBoosterSetParam(h_booster, "num_parallel_tree", "1");
It cost 800ms to make a prediction with 40 dim features of 1 data !!
more info: https://github.com/dmlc/xgboost/issues/3512