CODEWITHSUNDEEP
rroc
Faydey
Faydey

Reputation: 737

Obtaining threshold values from a ROC curve

I have some models, using ROCR package on a vector of the predicted class percentages, I have a performance object. Plotting the performance object with the specifications "tpr", "fpr" gives me a ROC curve.

I'm comparing models at certain thresholds of false positive rate (x). I'm hoping to get the value of the true positive rate (y) out of the performance object. Even more, I would like to get the class percentage threshold that was used to generate that point.

the index number of the false positive rate (x-value) that is closest to the threshold without being above it, should give me the index number of the appropriate true positive rate (y-value). I'm not exactly sure how to get that index value.

And more to the point, how do i get the threshold of class probabilities that was used to make that point?

Upvotes: 43

Views: 46181

Answers (5)

Enrique Pérez Herrero
Enrique Pérez Herrero

Reputation: 3882

Package pROC includes function coords for calculating the best threshold:

library(pROC)
my_roc <- roc(my_response, my_predictor)
coords(my_roc, "best", ret = "threshold")

Upvotes: 24

Adosey
Adosey

Reputation: 1

Following from Juilee Answer:

The definition of TPR and FPR in the dataframe are erroneous. Correcting them and re-posting the same answer as it is.

       threshold_data<-data.frame(FPR = 
                           threshold_data$false.positive.rate,
                   TPR = threshold_data$true.positive.rate, 
                   threshold = threshold_data$thresholds)

Upvotes: 0

Juilee Bhosale
Juilee Bhosale

Reputation: 11

Similar to @Artem's solution Basically the optimal threshold in a ROC curve is the widest part of the curve, or the point which gives maximum TPR while maintaining the lowest FPR FPR & TPR corresponding to best threshold - ROC curve

So one could also find the best threshold by finding the widest point or the point with maximum separation between TPR and FPR

Below is a quick solution using package ROSE

library(ROSE)
library(data.table)
threshold_data<-roc.curve(df$response,my_predictor,plotit = TRUE)
#Get TPR, FPR and corresponding threshold from roc.curve function and convert to dataframe 
threshold_data<-data.frame(TPR = threshold_data$false.positive.rate,
                       FPR = threshold_data$true.positive.rate,
                       threshold = threshold_data$thresholds)


# TPR       FPR  threshold       sep
# 1.0000000000 1.0000000       -Inf 0.0000000
# 0.7474009553 0.9820701 0.03405027 0.2346691
# 0.5869626300 0.9478403 0.08923265 0.3608776
# 0.4003933689 0.8777506 0.17368989 0.4773572
# 0.2225344198 0.7571312 0.25101859 0.5345968
# 0.1441416128 0.6495518 0.33035935 0.5054101
# 0.0868221411 0.5281174 0.44915920 0.4412952
# 0.0261309357 0.3390383 0.57857430 0.3129074
# 0.0089912897 0.2257539 0.76554635 0.2167626
# 0.0008429334 0.1140994 0.93730006 0.1132565
# 0.0000000000 0.0000000        Inf 0.0000000

threshold_data<-setDT(threshold_data)
threshold_data[,sep:=abs(FPR-TPR)]
best_threshold<-threshold_data[sep==max(sep),threshold]
#0.2510185

#Same result with package pROC
library(pROC)
my_curve <- roc(df$my_response,my_predictor)
coords(my_curve, "best", ret = "threshold")
#0.2510185

Upvotes: 1

Artem Klevtsov
Artem Klevtsov

Reputation: 9423

2 solutions based on the ROCR and pROC packages:

threshold1 <- function(predict, response) {
    perf <- ROCR::performance(ROCR::prediction(predict, response), "sens", "spec")
    df <- data.frame(cut = [email protected][[1]], sens = [email protected][[1]], spec = [email protected][[1]])
    df[which.max(df$sens + df$spec), "cut"]
}
threshold2 <- function(predict, response) {
    r <- pROC::roc(response, predict)
    r$thresholds[which.max(r$sensitivities + r$specificities)]
}
data(ROCR.simple, package = "ROCR")
threshold1(ROCR.simple$predictions, ROCR.simple$labels)
#> [1] 0.5014893
threshold2(ROCR.simple$predictions, ROCR.simple$labels)
#> [1] 0.5006387

See also OptimalCutpoints package which provides many algorithms to find an optimal thresholds.

Upvotes: 9

Zach
Zach

Reputation: 30311

This is why str is my favorite R function:

library(ROCR)
data(ROCR.simple)
pred <- prediction( ROCR.simple$predictions, ROCR.simple$labels)
perf <- performance(pred,"tpr","fpr")
plot(perf)
> str(perf)
Formal class 'performance' [package "ROCR"] with 6 slots
  ..@ x.name      : chr "False positive rate"
  ..@ y.name      : chr "True positive rate"
  ..@ alpha.name  : chr "Cutoff"
  ..@ x.values    :List of 1
  .. ..$ : num [1:201] 0 0 0 0 0.00935 ...
      ..@ y.values    :List of 1
      .. ..$ : num [1:201] 0 0.0108 0.0215 0.0323 0.0323 ...
  ..@ alpha.values:List of 1
  .. ..$ : num [1:201] Inf 0.991 0.985 0.985 0.983 ...

Ahah! It's an S4 class, so we can use @ to access the slots. Here's how you make a data.frame:

cutoffs <- data.frame([email protected][[1]], [email protected][[1]], 
                      [email protected][[1]])
> head(cutoffs)
        cut         fpr        tpr
1       Inf 0.000000000 0.00000000
2 0.9910964 0.000000000 0.01075269
3 0.9846673 0.000000000 0.02150538
4 0.9845992 0.000000000 0.03225806
5 0.9834944 0.009345794 0.03225806
6 0.9706413 0.009345794 0.04301075

If you have an fpr threshold you want to hit, you can subset this data.frame to find maximum tpr below this fpr threshold:

cutoffs <- cutoffs[order(cutoffs$tpr, decreasing=TRUE),]
> head(subset(cutoffs, fpr < 0.2))
          cut       fpr       tpr
96  0.5014893 0.1495327 0.8494624
97  0.4997881 0.1588785 0.8494624
98  0.4965132 0.1682243 0.8494624
99  0.4925969 0.1775701 0.8494624
100 0.4917356 0.1869159 0.8494624
101 0.4901199 0.1962617 0.8494624

Upvotes: 71

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