Interpreting NaN values for precision in Confusion Matrix Please refer to the confusion matrix here: https://imgur.com/a/Iq1epre
Would I get precision values of NaN because of 0/0 in the right most columns? Is that even possible? How should I interpret this?
Should I replace the NaN values for precision with zeros and still calculate metrics like a F-beta score? Or would that not be robust/lack statistical integrity?

Precision is the probability of being in a category given a prediction of that category, conditioning on the known prediction rather than on the unknown true state. (If you know  the true state, why do any fancy modeling that might get the wrong answer?)
If you never predict a particular category, precision makes less sense.
