Hello, and thanks for your reply.
The idea sounds good, but there is a problem with post-processing.
The reason I wanted it to return a default value is not for the output per-se, but more for the model to try to ignore the high amount of noise in the data, and only change the weights when it is sure it has high enough correlation with the data.
I have around 17 variables, and the output are 3 scores that go from 1 - 7.
Here I have assumed one of the variables doubles the chance of a bad score.
The default chance of guessing the correct score groups (1,2,3/4/5,6,7) is 1/3, and if the variable is contained, it increases to 1/2 (2/4).
This model has no way to go anywhere above 50% accuracy without overfitting.
That is why I wanted it to default to a value. I thought that if I condition the model to return a default value when it is not sure, I could reduce overfitting while also at the same time increasing precision.
But that gave me an idea. perphaps I am not preprocessing the data enough before feeding it to the model. I was too focused on the actual model.
So again, thanks for the help.