我在尝试在 Python 2.7 中实现这段代码时遇到了这个错误。希望能得到帮助。我使用的是最新的 sklearn 版本（0.18.1）和 xgboost 版本（0.6）。

import xgboost as xgbfrom sklearn.model_selection import StratifiedKFoldfrom sklearn.metrics import f1_score, roc_auc_score, confusion_matrixnfold = 3kf = StratifiedKFold(nfold, shuffle=True)dtrain = xgb.DMatrix(x_train, label=y_train)dtest = xgb.DMatrix(x_test)params = {    'objective' : 'binary:logistic',    'eval_metric': 'auc',    'min_child_weight':10,    'scale_pos_weight':scale,}hist = xgb.cv(params, dtrain, num_boost_round=10000, folds=kf, early_stopping_rounds=50, as_pandas=True, verbose_eval=100, show_stdv=True, seed=0)

我得到了这个错误：

TypeErrorTraceback (most recent call last)<ipython-input-52-41c415e116d7> in <module>()      5     'scale_pos_weight':scale,      6 }----> 7 hist = xgb.cv(params, dtrain, num_boost_round=10000, folds=kf, early_stopping_rounds=50, as_pandas=True, verbose_eval=100, show_stdv=True, seed=0)      8       9 /opt/conda/lib/python2.7/site-packages/xgboost/training.pyc in cv(params, dtrain, num_boost_round, nfold, stratified, folds, metrics, obj, feval, maximize, early_stopping_rounds, fpreproc, as_pandas, verbose_eval, show_stdv, seed, callbacks)    369     370     results = {}--> 371     cvfolds = mknfold(dtrain, nfold, params, seed, metrics, fpreproc, stratified, folds)    372     373     # setup callbacks/opt/conda/lib/python2.7/site-packages/xgboost/training.pyc in mknfold(dall, nfold, param, seed, evals, fpreproc, stratified, folds)    236         idset = [randidx[(i * kstep): min(len(randidx), (i + 1) * kstep)] for i in range(nfold)]    237     elif folds is not None:--> 238         idset = [x[1] for x in folds]    239         nfold = len(idset)    240     else:TypeError: 'StratifiedKFold' object is not iterable

回答：

在 xgb.cv 函数内部，尝试将

folds=kf

替换为

folds=list(kf.split(x_train,y_train))

使用 split 方法 来获取训练和验证的分割。我们将其转换为 list，这样它就变成了一个可迭代对象。

如果这样不行，尝试去掉 list。也就是：

folds=kf.split(x_train,y_train)