XGBRegressor hyperparameter optimization using xgb cv function I am trying to optimize hyper parameters of XGBRegressor using xgb's cv function and bayesian optimization (using hyperopt package). Here is the piece of code I am using for the cv part. 

dtrain = xgb.DMatrix(X_train, label=y_train)
cv_results = xgb.cv(params,dtrain,num_boost_round = 1000, folds= cv_folds, 
                        stratified = False, early_stopping_rounds = 100, metrics="rmse", seed = 44)


However, I am getting the following error within the xgb.cv function (part of the Trace):

414     cvfolds = mknfold(dtrain, nfold, params, seed, metrics, fpreproc,
--&gt; 415                       stratified, folds, shuffle)
    416 
    417     # setup callbacks

/anaconda3/envs/py36/lib/python3.6/site-packages/xgboost/training.py in mknfold(dall, nfold, param, seed, evals, fpreproc, stratified, folds, shuffle)
    261         except TypeError:
    262             # Custom stratification using Sklearn KFoldSplit object
--&gt; 263             splits = list(folds.split(X=dall.get_label(), y=dall.get_label()))
    264             in_idset = [x[0] for x in splits]
    265             out_idset = [x[1] for x in splits]

AttributeError: 'int' object has no attribute 'split' 


I can't figure out why am i getting this error. The documentation for xgboost is also not very clear and sparse. 
So any help would be greatly appreciated. 

Thanks
It seems to be complaining about the folds.split part, indicating that folds is an integer.  If your cv_folds object is just the number of folds, pass that as nfolds instead; folds expects the actual sklearn KFold object rather than a number.