xgb-wrapper交叉验证（xgb.cv）的结果是什么？

Question

我无法理解xgb.cv的输出： 1）是1倍或最佳k倍的结果？ 2）以及在训练集和测试集上将数据集拆分成什么原理-KFold或0.8 / 0.2？

运行代码时，我看到了计算过程。在获得最佳比分的较早停止回合后停止。

当然：＃个模型参数

num_parallel_tree = 1 
subsample = 1 
colsample_bytree = 0.4
objective = 'binary:logistic'
learning_rate = 0.05
eval_metric = 'auc'
max_depth = 10
min_child_weight = 4

n_estimators = 5000
seed = 7

#cross-validation parameters
nfold = 5
early_stopping_rounds = 5


bst_cv = xgb.cv(
    param, 
    dtrain,  
    num_boost_round=n_estimators, 
    nfold = nfold,
    early_stopping_rounds=early_stopping_rounds,
    verbose_eval=True
)

results:
[0] train-auc:0.910342+0.0015485    test-auc:0.850442+0.00619299
[1] train-auc:0.956268+0.00132653   test-auc:0.893746+0.00973467
...
[24]    train-auc:0.984302+0.000617268  test-auc:0.934326+0.00338043

然后-停止。