Hi,
My ANN’s predicts numerical values as output. The output layer is of length 4 (since it predicts 4 values from data). My input is X and Y, each with a length of 1000.
To put an uncertainty on each of my predicted values from my ANN, I want to apply jackknife. That is to evaluating my model several times and deleting one random datapoint in each evaluation. Thus, I would need to change my input layers (X and Y) to a length of 999.
Is it possible to do this? If not, why not?
Regards,
Stijn
Some insights/ideas would be very much appreciated here… 
It is not clear to me what you are aiming to. The layers shape is constant, and the reason is that each of the X values will be multiplied by a weight.
To evaluate if the net is doing well, just use a test set. Why isn’t this useful ?
Thanks for your reply.
Getting an uncertainty for each parameter is possible with K-fold validation (which I’m applying) but then I just get K predictions with a ton of computation/training time whereas with jackknife (or bootstrapping) I get multiple predictions by just using a single test, applying n evaluations with jackknife, and then appending the predictions each evaluation.
