I'm experimenting with ROC-AUC for binary classification problems. I want to generate synthetic data for a given AUC score. The inputs of the problem are the following:

  • A vector of labels, made of 0's and 1's.
  • A given AUC, let's say 0.8

I want to generate a vector of scores (preferably between 0 and 1) that, with these labels, has the given AUC. I know the solution is not unique but I cannot think a way to generate such vector of scores. An implementation in R would be also great.

  • $\begingroup$ In the WORST case, remember that you can use “brute force” and ask R to simulate the scores and solve the reminder of the problem (the measurement of AUC) for each of the n simulations, and stop the simulation loop for k<n iff the measured AUC for the scores simulated at k is in a range very close to 0.8. It will be time-consuming but if you initialize and guide the solver in a reasonable way then the time to get there will be reduced, especially if for each iteration the volume of data is not excessively big. It’s like saying: R will make the hard work for me and will try until it gets there $\endgroup$
    – Fr1
    Commented Aug 20, 2019 at 11:02
  • 2
    $\begingroup$ Coding questions are off-topic here, I'm afraid. $\endgroup$
    – mkt
    Commented Aug 26, 2019 at 7:44
  • $\begingroup$ This might be a shortcut. I worked on a similar project and based my simulation on the reasonably good assumption that AUC will have a decent linear correlation (although the quadratic fit is better) with the R-squared between the two variables -- the continuous "score" and the binary "prediction." $\endgroup$
    – rolando2
    Commented Aug 26, 2019 at 18:33
  • $\begingroup$ I think you made a mistake and by predictions, you mean labels/targets and the scores are the predictions right? $\endgroup$
    – rep_ho
    Commented Aug 29, 2019 at 20:04

1 Answer 1


There are multiple ways to do it. One is to assume to transform AUC to cohen's D and then just sample data from 2 standard normal distributions D standard deviations apart.

We can transform AUC to D according to a formula from SALGADO, Jesús F.. Transforming the Area under the Normal Curve (AUC) into Cohen’s d, Pearson’s r pb , Odds-Ratio, and Natural Log Odds-Ratio: Two Conversion Tables. The European Journal of Psychology Applied to Legal Context [online]. 2018, vol.10, n.1, pp.35-47. ISSN 1989-4007. http://dx.doi.org/10.5093/ejpalc2018a5

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Which in R code will work like this

auc <- 0.95

t <- sqrt(log(1/(1-auc)**2))
z <- t-((2.515517 + 0.802853*t + 0.0103328*t**2) / 
          (1 + 1.432788*t + 0.189269*t**2 + 0.001308*t**3))
d <- z*sqrt(2)

n <- 10000
x <- c(rnorm(n/2, mean = 0), rnorm(n/2, mean = d))
y <- c(rep(0, n/2), rep(1, n/2))

auc(roc(x, as.factor(y)))

# out
# [1] 0.9486257

Of course, since we are sampling, this will produce the correct AUC on average, but the specific sample will not be exactly the required AUC.


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