hello @michaelgloven - so for these kind of fundamental data science background topics I usually go "old school" with books (yes paper). My go-to texts are "Data Mining for the Masses" by Dr. Matthew North, and "Predictive Analytics and Data Mining" by Kotu & Deshpande. Both are excellent and are full of explicit examples using RapidMiner. For your question about ROC curves, Chapter 8 of Kotu & Deshpande is all about Model Evaluation which starts with a long explanation of ROC.
many thanks Scott. Figure 8.5 on page 269 of the Kotu book also has the ROC (thresholds) curve without explanation. It looks like the inverse of the ROC curve, probably a simple explanation, but still a mystery to me. I'll check out your second resource suggestion.
Each point of the ROC curve is the rate of true positives (or proportion of TP as it called in the first post) vs the rate of false positives (proportion of FP) for a specific applied threshold on the confidence of the corresponding classifier.
The ROC (thresholds) curve just shows this confidence threshold (sometimes also called confidence cut).
Hopes this helps,