Defining label in confusion matrix with highly imbalanced dataset
It is a binary classification problem. Usually the classes labeled as positive =1, and negative =0
I am currently working on building a neural net model that targets to predict success/failure of server update. 
However, the existing data is highly imbalanced. I.e. only 3 % of the records are failures, the rest is all success record.
I am now trying to do some data exploration using confusion matrix. In this case, should I assign 'positive(1)' label to 'failure' label? or does it even matter which label that I assign to ?
Thanks in advances.