[dsvav]

I am confused here , I don't understand what is final hypothesis here.

There are 1000 target function and corresponding 1000 weight vectors/hypothesis in problem 5 .

So for problem 6 , 1000 times I generate 1000 out-of-sample data and then for each weight vector and target function(from problem 5) I evaluate E_out for that out-of-sample data and finally average them. This is how I have done.

I don't see final hypothesis here , what I am missing , any hint

Could it be that in problem 5 there is supposed to be only one target function and many in-sample data ? If so then the final hypothesis/weights could be that produces minimum in-sample error E_in .

Please clarify.
Thanks a lot.