请教大家一个问题：

    我参考这个教程： http://www.feiguyunai.com/index.php/2017/11/19/pythonai-sklearn-customer-loss01/，用jupyter进行客户流失预测的分析，但是在交叉验证部分出现错误。主要步骤如下：
先定义函数run_cv：
from sklearn.model_selection import KFold
def run_cv(X,y,clf_class,**kwargs):
    # Construct a kfolds object
    kf = KFold(len(y),n_splits=5,shuffle=True)
    y_pred = y.copy()
    # Iterate through folds
    for train_index, test_index in kf:
        X_train, X_test = X[train_index], X[test_index]
        y_train = y[train_index]
        # Initialize a classifier with key word arguments
        clf = clf_class(**kwargs)
        clf.fit(X_train,y_train)
        y_pred[test_index] = clf.predict(X_test)
    return y_pred



然后运行：
def accuracy(y_true,y_pred):
    # NumPy interpretes True and False as 1. and 0.
    return np.mean(y_true == y_pred)

print( "Logistic Regression:")
print( "%.3f" % accuracy(y, run_cv(X,y,LR)))


报错如下：
Logistic Regression:


---------------------------------------------------------------------------TypeError                                 Traceback (most recent call last)<ipython-input-11-44c2d31e9f2a> in <module>      4       5 print( "Logistic Regression:")----> 6 print( "%.3f" % accuracy(y, run_cv(X,y,LR)))      7 print( "Gradient Boosting Classifier")      8 print( "%.3f" % accuracy(y, run_cv(X,y,GBC)))<ipython-input-9-d437442bb793> in run_cv(X, y, clf_class, **kwargs)      2 def run_cv(X,y,clf_class,**kwargs):      3     # Construct a kfolds object----> 4     kf = KFold(len(y),n_splits=5,shuffle=True)      5     y_pred = y.copy()      6     # Iterate through foldsTypeError: __init__() got multiple values for argument 'n_splits'


教程中kfold函数参数是n_folds，报错是没有这个参数；改为n_splits之后，报错就变成上面这样，想求教如何解决这个bug，谢谢解答。