Re: Exercise 4.10
Also I wanted to validate my explanation of other parts of this exercise.
For part (b), this is what I think:
As K increases, the estimation of outof sample error by validation error gets better. That explains the initial decrease in Expectation[OutofSample Error of g_(m*)]. Then, as K increases beyond the ‘optimal’ value, the training goes bad, which explains the rise.
Please let me know if my understanding is correct or not.
For part (a), I can't figure out the initial decrease in Expectation[OutofSample Error of g^_(m*)]. Any clue on this will be great.
Thanks,
Sayan
