title: ROC curve not looking right
body: <p>I have plotted ROC curves, using ROCR package, for a 2 class problem. According to my understanding, the curves should look like step changing plots at least for smaller datasets. My input is in fact small but the curves I get are essentially straight line looking. Is it because PROC fits a line through the curve or is it something else that I am missing? </p>
  
  <p>Input is here <a href="https://www.dropbox.com/s/z9ivg9kzlu1758w/sensor6data_f21_s5_with2Labels.csv?dl=0" rel="nofollow noreferrer">click me</a> and the code is as follows with the ROC part at the end. Appreciate your help!</p>
  
  <pre><code>library("caret")
  library("ROCR")
  sensor6data_s10_2class &lt;- read.csv("/home/sensei/clustering/sensor6data_f21_s10_with2Labels.csv")
  sensor6data_s10_2class &lt;- within(sensor6data_s10_2class, Class &lt;- as.factor(Class))
  sensor6data_s10_2class$Class2 &lt;- relevel(sensor6data_s10_2class$Class,ref="1")
  
  set.seed("4321")
  inTrain_s10_2class &lt;- createDataPartition(y = sensor6data_s10_2class$Class, p = .75, list = FALSE)
  training_s10_2class &lt;- sensor6data_s10_2class[inTrain_s10_2class,]
  testing_s10_2class &lt;- sensor6data_s10_2class[-inTrain_s10_2class,]
  y_s10 &lt;- testing_s10_2class[,22]
  
  ctrl_s10_2class &lt;- trainControl(method = "repeatedcv", number = 10, repeats = 10 , savePredictions = TRUE)
  model_train_multinom_s10_2class &lt;- train(Class2 ~ ZCR + Energy + SpectralC + SpectralS + SpectralE + SpectralF + SpectralR + MFCC1 + MFCC2 + MFCC3 + MFCC4 + MFCC5 + MFCC6 + MFCC7 + MFCC8 + MFCC9 + MFCC10 + MFCC11 + MFCC12 + MFCC13, data = training_s10_2class, method="multinom", trControl = ctrl_s10_2class)
  pred_multinom_s10_2class = predict(model_train_multinom_s10_2class, newdata=testing_s10_2class)
  
  pred2_s10 &lt;- prediction(as.numeric(as.character(pred_multinom_s10_2class)), as.numeric(as.character(y_s10)))
  perf2_s10 &lt;- performance(pred2_s10, "tpr", "fpr")
  plot(perf2_s10,col='magenta',lwd=3)
  </code></pre>