So actually I think you want to be using the other approach for dealing with rare targets, which is to adjust the posterior probabilities instead of entering the decision weights (those only affect profit, not other fit statistics). So do that, in the Decisions node, you would no longer use the inverse priors on the diagonal of the decision matrix but just revert those to 1's, then you want to click Refresh on the Targets tab, then on the Prior Probabilities tab, enter the original priors for your target (the very rare proportion for your event, e.g.). Now this will apply an adjustment to your posterior probabilities - hopefully you will see better results this way.
To answer your other question, EM_CLASSIFICATION is the generically named variable containing the predictions based on your model. Here are more details about those variables from the Score node:
EM_PROBABILITY
Probability of Classification
Posterior probability associated with the predicted classification. That is, it corresponds the maximum of the posterior probabilities, max(P1, P2, ..., Pk).
EM_EVENTPROBABILITY
Probability for level n of vnm
Posterior probability associated with target event.
EM_CLASSIFICATION
Prediction for vnm
I_variable, the prediction variable for a class target.
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