Question

我正在为一个极不平衡的数据集工作，总共有44个样本用于我的研究项目。这是我使用“留一法交叉验证”的少数类的3/44个样本的二进制分类问题。如果我在LOOCV循环之前对整个数据集执行SMOTE过采样，则ROC曲线的预测准确性和AUC分别接近90％和0.9。但是，如果我仅对LOOCV循环内的训练集进行过采样，这恰好是更合乎逻辑的方法，则ROC曲线的AUC会低至0.3

我还尝试了精确调用曲线和分层的k倍交叉验证，但是由于在循环内外进行过采样，结果也面临类似的区别。请建议我在什么地方进行过度采样，并在可能的情况下说明区别。

循环内过度采样：-

i=0
acc_dec = 0
y_test_dec=[] #Store y_test for every split
y_pred_dec=[] #Store probablity for positive label for every split

for train, test in loo.split(X):    #Leave One Out Cross Validation
    #Create training and test sets for split indices
    X_train = X.loc[train]  
    y_train = Y.loc[train]
    X_test = X.loc[test]
    y_test = Y.loc[test]

    #oversampling minority class using SMOTE technique
    sm = SMOTE(sampling_strategy='minority',k_neighbors=1)
    X_res, y_res = sm.fit_resample(X_train, y_train)

    #KNN
    clf = KNeighborsClassifier(n_neighbors=5) 
    clf = clf.fit(X_res,y_res)
    y_pred = clf.predict(X_test)
    acc_dec = acc_dec +  metrics.accuracy_score(y_test, y_pred)
    y_test_dec.append(y_test.to_numpy()[0])
    y_pred_dec.append(clf.predict_proba(X_test)[:,1][0])
    i+=1

# Compute ROC curve and ROC area for each class
fpr,tpr,threshold=metrics.roc_curve(y_test_dec,y_pred_dec,pos_label=1)
roc_auc = metrics.auc(fpr, tpr)
print(str(acc_dec/i*100)+"%")

AUC：0.25

准确度：68.1％

循环外过采样：

acc_dec=0 #accuracy for decision tree classifier
y_test_dec=[] #Store y_test for every split
y_pred_dec=[] #Store probablity for positive label for every split
i=0
#Oversampling before the loop
sm = SMOTE(k_neighbors=1)
X, Y = sm.fit_resample(X, Y)   
X=pd.DataFrame(X)
Y=pd.DataFrame(Y)
for train, test in loo.split(X):    #Leave One Out Cross Validation

    #Create training and test sets for split indices
    X_train = X.loc[train]  
    y_train = Y.loc[train]
    X_test = X.loc[test]
    y_test = Y.loc[test]

    #KNN
    clf = KNeighborsClassifier(n_neighbors=5) 
    clf = clf.fit(X_res,y_res)
    y_pred = clf.predict(X_test)
    acc_dec = acc_dec +  metrics.accuracy_score(y_test, y_pred)
    y_test_dec.append(y_test.to_numpy()[0])
    y_pred_dec.append(clf.predict_proba(X_test)[:,1][0])
    i+=1

# Compute ROC curve and ROC area for each class
fpr,tpr,threshold=metrics.roc_curve(y_test_dec,y_pred_dec,pos_label=1)
roc_auc = metrics.auc(fpr, tpr)
print(str(acc_dec/i*100)+"%")

AUC：0.99

准确度：90.24％

这两种方法如何导致如此不同的结果？我该怎么办？

Answer 1

在拆分数据之前进行上采样（例如SMOTE）意味着测试集中会出现有关训练集的信息。有时称为“泄漏”。不幸的是，您的第一个设置是正确的。

Here's a post解决了这个问题。

留出一分交叉验证的超采样

1 个答案: