How to pool ROC curves in R to better understand a model's performance (CC135)

August 9, 2021 • PD Schloss • 2 min read

In this Code Club, Pat shows how he would pool ROC curves so that you can directly assess a model’s sensitivity for specificity. The area under the receiver operator characteristic (ROC) curve (AUC) is a useful metric of performance, but it isn’t always the best way to assess performance since it looks over all possible specificities. The challenge is that with the mikropml framework we get one ROC curve per 80/20 training-testing split and we need to pool the curves to get a composite ROC curve. Even if you don’t care about ROC curves, this episode is sure to have a lot of value for you including a little known R tip towards the end of the episode!

Pat will use functions from the mikropml R package and the ggplot2 and dplyr packages in RStudio.


You can browse the state of the demo at the


If you haven’t been following along, you can get caught up by doing the following:

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