title: Plotting the ROC curve of K-fold Cross Validation
body: <p>I am working with an imbalanced dataset. I have applied SMOTE Algorithm to balance the dataset after splitting the dataset into test and training set before applying ML models. I want to apply cross-validation and plot the ROC curves of each folds showing the AUC of each fold and also display the mean of the AUCs in the plot. I named the resampled training set variables as X_train_res and y_train_res and following is the code:</p>
  <pre><code>cv = StratifiedKFold(n_splits=10)
  classifier = SVC(kernel='sigmoid',probability=True,random_state=0)
  
  tprs = []
  aucs = []
  mean_fpr = np.linspace(0, 1, 100)
  plt.figure(figsize=(10,10))
  i = 0
  for train, test in cv.split(X_train_res, y_train_res):
      probas_ = classifier.fit(X_train_res[train], y_train_res[train]).predict_proba(X_train_res[test])
      # Compute ROC curve and area the curve
      fpr, tpr, thresholds = roc_curve(y_train_res[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, tpr, lw=1, alpha=0.3,
               label='ROC fold %d (AUC = %0.2f)' % (i, roc_auc))
  
      i += 1
  plt.plot([0, 1], [0, 1], 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),
           lw=2, 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([-0.01, 1.01])
  plt.ylim([-0.01, 1.01])
  plt.xlabel('False Positive Rate',fontsize=18)
  plt.ylabel('True Positive Rate',fontsize=18)
  plt.title('Cross-Validation ROC of SVM',fontsize=18)
  plt.legend(loc=&quot;lower right&quot;, prop={'size': 15})
  plt.show()
  </code></pre>
  <p>following is the output:</p>
  <p><a href="https://i.stack.imgur.com/fcqqV.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/fcqqV.png" alt="enter image description here" /></a></p>
  <p>Please tell me whether the code is correct for plotting ROC curve for the cross-validation or not.</p>