Is it possible to find the confidence level of classifier scores?

I have a writer recognition system that gives back an NLL (Negative Least Likelihood) score for a test sample against every trained model. For example if there are thirteen models to compare the sample against the NLL output will look like this.

80815.7010223003  85581.6346832070  83606.7426608412  85056.4654552773  87063.0759166968  85087.6025451967  83785.5540344493  81874.6972713527  84314.5367319622  89551.5734673822  81328.8830385541  89110.5651289799  83632.8519473224

Where each column represents the score for that sample against every model. Column 1 gives the score against model 1 and so on.This test sample is written by model 1. So the first column should have the minimum value for correct prediction.The output I provided here gives the desired prediction, as the value of column 1 is minimum.

Here is the score from another classifier for the same test sample:

13229.1198655118  15825.1707083033  14812.4282777584  14511.5846921635  20356.4908375808  17776.3907333853  15387.8566384908  14271.7361799156  16061.8498237708  19712.8938234106  12761.0017859257  15197.7126488257  15778.5860607059

This classifier is incorrectly classifying this sample to belong to class 11. Whereas the classifier shown before this correctly classified it.

Now naturally the best way for me would be select the correct score. I was reading up and found out that one way to do this would be to find out the confidence level of the scores presented by every classifier. And the score having the better confidence level would be the most likely output. I did some reading and came upon this answer as well but this does not appear to be my solution.

How exactly would I go about determining the confidence level of my score? Or the best way to select the score?

Thank you