title: ROC curve only has few points
body: <p>I have a training set that has 141 objects and a testing set that has 15 objects. I trained a SVM classifier and would like to plot the ROC curve to visualize the performance. The codes I used are:</p>
  <pre><code>training_fold = feature.selected.matrix[-x,] # training fold =  training set minus (-) it's sub test fold
  test_fold = feature.selected.matrix[x,] # here we describe the test fold individually
  
  classifier = svm(formula = Outcome ~ .,
                 data = training_fold,
                 type = 'C-classification',
                 kernel = 'radial',
                 gamma = 0.01,
                 cost = 3, decision.value = TRUE)      
  y_pred = predict(classifier, newdata = test_fold[,-ncol(test_fold)],  decision.value = TRUE)
  
  
  fitted = attributes(y_pred)$decision.values  
  
  predob = prediction(fitted, test_fold$Outcome)
  
  
  perf = performance(pred , 'fpr', 'tpr')
  perf
  plot(perf)   #dev.off()
  </code></pre>
  <p>This gives a plot that contains only 16 points, which makes the curve not smooth.
  <a href="https://i.stack.imgur.com/XIsRe.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/XIsRe.png" alt="enter image description here" /></a>
  Also, the AUC is extremely small (~0.18), even though the accuracy is over 80%. Can anyone help me explain this?</p>