Hi @MarcB -
In your workflow, you are generating a ROC curve for each model and then attempting to join them. But if you carefully look at the outputs from the data ports on those nodes, you’ll see that what is available is the overall AUC.
You might want to take a look at this workflow that shows how you can plot multiple ROC results on a single curve:
Here, the probabilities for each model are generated, and those columns are joined into a single table prior to display in a ROC curve node.
You may also want to consider using the new Binary Classification Inspector node to display an interactive view of multiple model results - both ROC curves as well as other metrics, and a confusion matrix too.
As to your second question, I think you would have to calculate this manually - as far as I know the p-value is not currently an available output for the individual AUCs.