Can someone explain me please how to plot a ROC curve with ROCR. I know that I should first run:
prediction(predictions, labels, label.ordering = NULL)
and then:
performance(prediction.obj, measure, x.measure="cutoff", ...)
I am just not clear what is meant with prediction and labels. I created a model with ctree and cforest and I want the ROC curve for both of them to compare it in the end. In my case the class attribute is y_n, which I suppose should be used for the labels. But what about the predictions? Here are the steps of what I do (dataset name= bank_part):
pred<-cforest(y_n~.,bank_part)
tablebank<-table(predict(pred),bank_part$y_n)
prediction(tablebank, bank_part$y_n)
After running the last line I get this error:
Error in prediction(tablebank, bank_part$y_n) :
Number of cross-validation runs must be equal for predictions and labels.
Thanks in advance!
Here's another example: I have the training dataset(bank_training) and testing dataset(bank_testing) and I ran a randomForest as below:
bankrf<-randomForest(y~., bank_training, mtry=4, ntree=2,
keep.forest=TRUE,importance=TRUE)
bankrf.pred<-predict(bankrf, bank_testing, type='response')
Now the bankrf.pred is a factor object with labels c=("0", "1"). Still, I don't know how to plot ROC, cause I get stuck to the prediction part. Here's what I do
library(ROCR)
pred<-prediction(bankrf.pred$y, bank_testing$c(0,1)
But this is still incorrect, cause I get the error message
Error in bankrf.pred$y_n : $ operator is invalid for atomic vectors
Answer
The predictions are your continuous predictions of the classification, the labels are the binary truth for each variable.
So something like the following should work:
> pred <- prediction(c(0.1,.5,.3,.8,.9,.4,.9,.5), c(0,0,0,1,1,1,1,1))
> perf <- performance(pred, "tpr", "fpr")
> plot(perf)
to generate an ROC.
EDIT: It may be helpful for you to include the sample reproducible code in the question (I'm having a hard time intepreting your comment).
There's no new code here, but... here's a function I use quite often for plotting an ROC:
plotROC <- function(truth, predicted, ...){
pred <- prediction(abs(predicted), truth)
perf <- performance(pred,"tpr","fpr")
plot(perf, ...)
}