交叉验证分类错误

Question

我正在使用以下代码获取分类结果：

 folds = 5 #number of folds for the cv
        #Logistic Regression--
        clf = linear_model.LogisticRegression(penalty='l1')
        kf = KFold
(len(clas), n_folds=folds)
    fold = 1
    cms = np.array([[0,0],[0,0]])
    accs = []
    aucs=[]
    for train_index, test_index in kf:
        X_train, X_test = docs[train_index], docs[test_index]
        y_train, y_test = clas2[train_index], clas2[test_index]
        clf.fit(X_train, y_train)
        prediction = clf.predict(X_test)
        acc = accuracy_score(prediction, y_test)
        cm = confusion_matrix(y_test,prediction)
        pred_probas = clf.predict_proba(X_test)[:,1]
        fpr, tpr, thresholds = metrics.roc_curve(y_test, pred_probas)
        print('Test Accuracy for fold {}: {}n{}'.format(fold,round((acc*100),2),cm))
        roc_auc = auc(fpr,tpr)
        print('AUC for fold {} : {}'.format(fold,round((roc_auc*100),2)))
        fold +=1
        cms += cm
        accs.append(acc)
        aucs.append(roc_auc)
    print('CV test accuracy: {}n{}'.format(round((np.mean(accs)*100),2),cms))
    print('nCV AUC: {}'.format(round(np.mean(aucs)*100),2))
    print('nCV accuracy: %.3f +/- %.3f' % (round((np.mean(accs)*100),2),round((np.std(accs)*100),2)))
    print('nCV ROC AUC: %.3f +/- %.3f' % (round((np.mean(aucs)*100),2),round((np.std(aucs)*100),2)))
    print('nPeak accuracy: '+str(round((np.amax(accs)*100),2)))
    print('nPeak ROC AUC: '+str(round((np.amax(aucs)*100),2)))

我不确定我是否在做某事，但我有2堂课是YES = 406否= 139，代码给我遵循结果

Test Accuracy for fold 1: 87.16
[[94  9]
 [ 5  1]]
AUC for fold 1 : 66.1
Test Accuracy for fold 2: 92.66
[[100   6]
 [  2   1]]
AUC for fold 2 : 62.42
Test Accuracy for fold 3: 90.83
[[99  7]
 [ 3  0]]
AUC for fold 3 : 43.08
Test Accuracy for fold 4: 88.07
[[83  8]
 [ 5 13]]
AUC for fold 4 : 85.5
Test Accuracy for fold 5: 53.21
[[ 0  0]
 [51 58]]
AUC for fold 5 : nan
CV test accuracy: 82.39
[[376  30]
 [ 66  73]]
CV AUC: nan
CV accuracy: 82.390 +/- 14.720
CV ROC AUC: nan +/- nan
Peak accuracy: 92.66
Peak ROC AUC: nan
C:UserskkothariAppDataLocalContinuumAnaconda3libsite-packagessklearnmetricsranking.py:530: UndefinedMetricWarning: No negative samples in y_true, false positive value should be meaningless
  UndefinedMetricWarning)
C:UserskkothariAppDataLocalContinuumAnaconda3libsite-packagessklearnmetricsranking.py:95: RuntimeWarning: invalid value encountered in less
  if np.any(dx < 0):

最初我只有17个文档，但是很好。

基本上您有一个很小的类（大约20-30个样本？），而在其中一个分裂中，您没有得到任何，因此导致错误。您可以改用stratifiedkfold，这可以确保在每个班级中都有每个类别的样本数量。