[240] train-logloss:0.263565 valid-logloss:0.392514
[250] train-logloss:0.261231 valid-logloss:0.392377
[260] train-logloss:0.257999 valid-logloss:0.392149
[270] train-logloss:0.254814 valid-logloss:0.39179
[280] train-logloss:0.251346 valid-logloss:0.39179
[290] train-logloss:0.248382 valid-logloss:0.391635
[300] train-logloss:0.245682 valid-logloss:0.392021
[310] train-logloss:0.243229 valid-logloss:0.392104
[320] train-logloss:0.241036 valid-logloss:0.392591
Stopping. Best iteration:
[292] train-logloss:0.247746 valid-logloss:0.391567
如果训练时设置了early_stopping_rounds参数，则可以
ypred = bst.predict(dtest, ntree_limit=bst.best_ntree_limit)
bst是训练好的模型
我是参看
https://xgboost.readthedocs.io/en/latest/python/python_intro.html
