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Specificity, sensitivity and AUC measures via RM v9.1
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Telcontar120 Moderator, RapidMiner Certified Analyst, RapidMiner Certified Expert, Member Posts: 1,635
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ROC / AUC and its related measures are designed for binominal label classification problems only.
The best way to approach if you have multiple classes is to encode with a one-vs-all-others approach. So if you have possible label values {1,2,3} then encode that as 1 vs not 1, and generate the ROC. You can repeat for the other classes as needed, store the performances, and average them as well if you want.7 -
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Telcontar120 Moderator, RapidMiner Certified Analyst, RapidMiner Certified Expert, Member Posts: 1,635
Unicorn
Sensitivity is the true positive rate, and specificity is the true negative rate. Both are also available in the Performance (Binominal classification) operator in RapidMiner so you should be able to get them in the same way you get the AUC above, by using the 1 vs all others comparison method for a multi-class problem.
Here's a basic description of the concepts in case you need a refresher: https://en.wikipedia.org/wiki/Sensitivity_and_specificity5
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