我正在尝试编写代码，其中，数据集分为训练和测试。火车的主要部分将分为火车验证和交叉验证。使用K-fold值，我想运行代码，以便将主训练集划分为任意数量的折叠（或组，具体取决于我们提到的折叠值），然后将（folds-1）组分配给划分后的训练集和其余的要交叉验证。例如，如果有3折，则火车将分为三组：g1，g2，g3。首先，以g1 + g2作为训练，g3为交叉验证，然后，以g2 + g3为训练，g1为交叉验证，依此类推，以求出准确度，从而得出最佳K值。

我使用的逻辑是根据折叠次数划分主火车，并在其上使用随机选择。因此，从“ split_list”功能中选择了一组，然后进行交叉验证。其余部分用于训练（主要火车交叉验证）。

随机导入 从sklearn.metrics导入precision_score

def split_list（x_train，folds）：

length = len(x_train)

return  random.choices([ x_train[i*length // folds: (i+1)*length // folds] 
         for i in range(folds) ])
def Random_Search(x_train,y_train,classifier,params,folds):

trainscores = []
cvscores = []

for k in tqdm(params['n_neighbors']):

    trainscores_folds = []
    cvscores_folds = []

    for j in range(0,folds):   

        cv_indices = split_list(list(x_train),folds)
        train_indices = list(set(list(range(1,len(x_train)))) - 
                         set(cv_indices))

我遇到的错误：

TypeError                                 Traceback (most recent call last)
<ipython-input-114-808deaf8461e> in <module>

      8 params = {'n_neighbors':sorted(random.sample(range(1,50),10))}

      9 folds = 9

---> 10 trainscores,cvscores = Random_Search(X_train,neigh,folds)

     11 plt.plot(params['n_neighbors'],trainscores,label='train cruve')

     12 plt.plot(params['n_neighbors'],cvscores,label='cv cruve')

<ipython-input-113-fc0b09f4ad82> in Random_Search(x_train,folds)

     14         for j in range(0,folds):

     15             cv_indices = split_list(list(x_train),folds)

---> 16             train_indices = list(set(list(range(1,len(x_train)))) - set(cv_indices))

     17 # selecting the data points based on the train_indices and test_indices

     18             X_train = x_train[train_indices]

TypeError: unhashable type: 'list'