I have a deep learning classification pipeline in which I had created 10 independent train/val/test splits. The pipeline uses large images which must be broken up into "tiles" which are assigned the label of the original image. I have a script which gives me the AUC and 95% boostrapped confidence interval on a given test set. After running on all 10 splits, I have 10 AUCs and 10 CIs. I was wondering if there was a proper way to aggregate these and generate 1 single statistic AUC and CI.
Intuitively I would think to get the mean AUC, the mean lower bound CI and mean upper bound CI, but I am not sure if that is correct and what to call that if it is correct.