I am struggling to understand how to calculate

in this question. I have two competing theories, which I will describe below. Any help is greatly appreciated.

Once the algorithm terminates, I have

. I now generate a new set of data points

. Using my original target function to generate the corresponding

.

Case 1. Just use the same cross entropy error calculation but on this new data set.

Case 2. Directly calculate the expected output of our hypothesis function and compare to

.

with probability

Ultimately this gives us the probability that our hypothesis aligns with Y:

In the lectures/book, we would multiply these probabilities to get the "likelihood" that the data was generated by this hypothesis. However, it seems that averaging over these should give the expected error in this sample.

It feels as though the first approach is the correct one, but I struggle because the second approach makes intuitive sense since that is how I historically I would have calculated

. To make matters worse, the two approaches very closely approximate different answers in the question!