Suppose my training data contains ~100 variables, and each example is tagged as "success" or "failure".
I understand how a neural network can be used to try and predict success vs failure based on the variables.
However I am interested in the neural network outputting the posterior probability rather than success or failure. In fact, I evaluate the efficacy of the NN based on how accurate the probabilities are (eg. AUROC over the entire dataset) rather than % of cases with correct prediction.
Are NN's the right tool for the job here and if so how do you structure the NN to output this?
(NOTE: I'm a ML newbie!)