我正在Datacamp Extreme Gradient Boosting with XGBoost 上执行一个教程，我对一个结果有点困惑。
执行以下代码时
# Create your housing DMatrix:
housing_dmatrix = xgb.DMatrix(data=data, label=y)
# Create the parameter dictionary for each tree: params
params = {"objective":"reg:linear", "max_depth":4}
# Perform cross-validation with early stopping: cv_results
cv_results = xgb.cv(dtrain=housing_dmatrix,params=params,nfold=3, num_boost_round=50, early_stopping_rounds=10, metrics="rmse", as_pandas=True, seed=123)
# Print cv_results
print(cv_results)
mean_mae = cv_results['test-rmse-mean'].min()
boost_rounds = cv_results['test-rmse-mean'].idxmin()
print("\tRMSE {} for {} rounds".format(mean_mae, boost_rounds))
我得到这个输出：
test-rmse-mean test-rmse-std train-rmse-mean train-rmse-std
0 142644.104167 705.732300 141861.109375 396.179855
1 104867.638021 109.049658 103035.130209 47.104957
2 79261.453125 …