我需要对不平衡样本进行分类（class_1：class_0 = 1：9），每个样本具有6个特征。我使用SVM和k倍CV（k = 10）进行分类。对于折叠，在训练数据集中，我使用ADASYN对少数派进行了过度采样。但是最终的AUC结果表明，每个折叠的AUC差异很大。代码如下。

import numpy as np
import matplotlib.pyplot as plt

from sklearn.preprocessing import StandardScaler
from imblearn.over_sampling import SMOTE,ADASYN
from collections import Counter
from sklearn import svm
from sklearn.model_selection import StratifiedKFold
from sklearn.metrics import roc_curve,auc
from scipy import interp

# =============================================================================
# load result
# =============================================================================
class_1 = np.load('class_1.npy')
class_0 = np.load('class_0.npy')

class_1_0 = np.concatenate((class_1,class_0),axis=0)
# =============================================================================
# classification
# =============================================================================
X = StandardScaler().fit_transform(class_1_0)
y = np.hstack([np.ones((1260,),dtype=np.int),np.zeros((11340,dtype=np.int)]) # label y int format

# =============================================================================
# k-fold Cross-validation with ADASYN and SVM
# =============================================================================
random_state = np.random.RandomState(0)
cv = StratifiedKFold(n_splits=10)
clf = svm.SVC(kernel='linear',C=1,probability=True,random_state=random_state)
tprs = []
aucs = []
mean_fpr = np.linspace(0,1,100)

fig = plt.figure(figsize=(10,8))

i = 0
for train,test in cv.split(X,y):
    X_train_oversampled,y_train_oversampled = ADASYN().fit_sample(X[train],y[train]) 
    print(sorted(Counter(y_train_oversampled).items()))
    probas_ = clf.fit(X_train_oversampled,y_train_oversampled).predict_proba(X[test])

    # Compute ROC curve and area the curve
    fpr,tpr,thresholds = roc_curve(y[test],probas_[:,1])
    tprs.append(interp(mean_fpr,fpr,tpr))
    tprs[-1][0] = 0.0
    roc_auc = auc(fpr,tpr)
    aucs.append(roc_auc)
    plt.plot(fpr,lw=1,alpha=0.3,label='ROC fold %d (AUC = %0.2f)' % (i,roc_auc))
    i += 1
plt.plot([0,1],[0,linestyle='--',lw=2,color='r',label='Chance',alpha=.8)

mean_tpr = np.mean(tprs,axis=0)
mean_tpr[-1] = 1.0
mean_auc = auc(mean_fpr,mean_tpr)
std_auc = np.std(aucs)
plt.plot(mean_fpr,mean_tpr,color='b',label=r'Mean ROC (AUC = %0.2f $\pm$ %0.2f)' % (mean_auc,std_auc),alpha=.8)

std_tpr = np.std(tprs,axis=0)
tprs_upper = np.minimum(mean_tpr + std_tpr,1)
tprs_lower = np.maximum(mean_tpr - std_tpr,0)
plt.fill_between(mean_fpr,tprs_lower,tprs_upper,color='grey',alpha=.2,label=r'$\pm$ 1 std. dev.')

plt.xlim([-0.05,1.05])
plt.ylim([-0.05,1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Example')
plt.legend(loc="lower right")
plt.show()