如果您看到示例

在下面的十字线中使用'neg_mean_squared_error'进行得分

X = array[:,0:13]
Y = array[:,13]
seed = 7
kfold = model_selection.KFold(n_splits=10,random_state=seed)
model = LinearRegression()
scoring = 'neg_mean_squared_error'
results = model_selection.cross_val_score(model,X,Y,cv=kfold,scoring=scoring)
print("MSE: %.3f (%.3f)") % (results.mean(),results.std())

但是在下面的xgboost示例中，我正在使用metrics ='rmse'

cmatrix = xgb.DMatrix(data=X,label=y)
params = {'objective': 'reg:linear','max_depth': 3}
cv_results = xgb.cv(dtrain=cmatrix,params=params,nfold=3,num_boost_round=5,metrics='rmse',as_pandas=True,seed=123)

print(cv_results)