Why does my ROC curve have a sharp edge? I was working on a random forest model in R and I got a ROC curve that looks like this. This is very odd since there is no curvature. The data does have mostly qualitative features with only 2-3 quantitative features. The data is also mostly one class (i.e. 80% of my data is in one class and 20% in the second class). It seems to favor sensitivity over specificity. The other issue is that the performance says that it only has 3 data points. I have a lot of data so I am not sure why it says only 3 points. Could someone explain what is happening with this ROC curve and why there is no curvature?
This is my code for the ROC curve plot.
#Test
pred <- ROCR::prediction(as.numeric(predict(forest.out, newdata = 
x_test, type =  "response")), labels = as.numeric(y_test$Success))
perf <- performance(pred, measure = "tpr", x.measure = "for")

#Plot
plot(perf, xlab = "1-specificity", ylab = "sensitivity", main = "ROC 
curve")
abline(0,1, col = "grey")
plot(perf, add = TRUE, col = "red")




I was able to plot the ROC curve by a different method. I have the code below. I am still not sure why the code I had previously would not work.
library(pROC)
rf.roc<-roc(y_test$Success, forest.out$test$votes[,2])
plot(rf.roc, 
 col="red", 
 lwd=3, 
 main="ROC Curve",)
auc(rf.roc)


