Hi all,
I have a (huge) theoretical question connected with a regression problem of neural network:  what approach should I follow if my training dataset is incompleted and or my measurements are affected by strong biases?
let’s assume I want my net to predict the Y variable given the X,W, Z variables. Unfortunately I know that Y depends also on another variable, K, of whom I have no records. Of course the training dataset is essential to produce a “optimal” net, but I am wondering if there is any approach that considers, I don’t know, the inclusion of a stochastic variable that may take into account this lack of data.
Connected with the previous: what if my training dataset is not exact? how can I consider the uncertainty related to my measurements in training my net?
thank you!