我查看了文档，但没有找到我问题的答案，希望这里有人知道。以下是一些示例代码：
N_FOLDS= 5model = lgb.LGBMClassifier()default_params = model.get_params()#覆盖一个参数default_params['objective'] = 'regression'cv_results = lgb.cv(default_params, train_set, num_boost_round = 100000, nfold = N_FOLDS, early_stopping_rounds = 100, metrics = 'rmse', seed = 50, stratified=False)我得到了一个包含6个不同值的字典，像这样：
{'rmse-mean': [635.2078190031074, 632.0847253839236, 629.6661071275558, 627.9721515847672, 626.6712284533291, 625.293530527769], 'rmse-stdv': [197.5088741303537, 198.66960690389863, 199.56134068525006, 200.25929541235243, 200.8251430042537, 201.50213772830526]}起初，我以为字典中的值对应于每个折叠的RMSE（在本例中为5个），但似乎并非如此。这个字典看起来是按RMSE值降序排列的。
有谁知道每个值对应什么吗？
回答：
这些值不对应于折叠，而是对应于每次提升轮次的交叉验证结果（所有测试折叠的RMSE均值）。如果你只进行5轮并打印每轮的结果，就可以非常清楚地看到这一点：
import lightgbm as lgbfrom sklearn.datasets import load_bostonX, y = load_boston(return_X_y=True)train_set = lgb.Dataset(X,label = y)params = {'learning_rate': 0.05,'num_leaves': 4,'subsample': 0.5}cv_results = lgb.cv(params, train_set, num_boost_round = 5, nfold = N_FOLDS, verbose_eval = True, early_stopping_rounds = None, metrics = 'rmse', seed = 50, stratified=False)[LightGBM] [Info] Total Bins 1251[LightGBM] [Info] Number of data points in the train set: 404, number of used features: 13[LightGBM] [Info] Start training from score 22.585149[LightGBM] [Info] Start training from score 22.109406[LightGBM] [Info] Start training from score 22.579703[LightGBM] [Info] Start training from score 22.784158[LightGBM] [Info] Start training from score 22.599010[1] cv_agg's rmse: 8.86903 + 0.88135[2] cv_agg's rmse: 8.58355 + 0.860252[3] cv_agg's rmse: 8.31477 + 0.842578[4] cv_agg's rmse: 8.06201 + 0.82627[5] cv_agg's rmse: 7.8268 + 0.800053import pandas as pdpd.DataFrame(cv_results) rmse-mean rmse-stdv0 8.869030 0.8813501 8.583552 0.8602522 8.314774 0.8425783 8.062014 0.8262704 7.826800 0.800053在你的帖子中，你设置了early_stopping_rounds = 100，并使用了默认的learning rate = 0.1，这可能根据你的数据来说有点高，因此很可能在6轮后就停止了。
使用上面的相同示例，你可以看到如果我们设置early_stopping_rounds = 100，它会在每100轮评估指标的改进情况，并返回停止前100轮的结果：
cv_results = lgb.cv(params, train_set, num_boost_round = 2000, nfold = N_FOLDS, verbose_eval = True,early_stopping_rounds = 100, metrics = 'rmse',seed = 50, stratified=False)[...][1475] cv_agg's rmse: 3.20605 + 0.50213[1476] cv_agg's rmse: 3.20616 + 0.501997[1477] cv_agg's rmse: 3.20607 + 0.501998[1478] cv_agg's rmse: 3.20636 + 0.501865[1479] cv_agg's rmse: 3.20631 + 0.501905[1480] cv_agg's rmse: 3.20633 + 0.501731[1481] cv_agg's rmse: 3.20659 + 0.501494[1482] cv_agg's rmse: 3.2068 + 0.502046[1483] cv_agg's rmse: 3.20687 + 0.50213[1484] cv_agg's rmse: 3.20701 + 0.502265[1485] cv_agg's rmse: 3.20717 + 0.502096[1486] cv_agg's rmse: 3.2072 + 0.501779[1487] cv_agg's rmse: 3.20722 + 0.501613[1488] cv_agg's rmse: 3.20718 + 0.501308[1489] cv_agg's rmse: 3.20701 + 0.501232pd.DataFrame(cv_results).shape(1389, 2)如果你想要估计你的模型的RMSE值，请取最后一个值。
