[sayan751]

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 out-of sample error by validation error gets better. That explains the initial decrease in Expectation[Out-of-Sample 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[Out-of-Sample Error of g^-_(m*)]. Any clue on this will be great.

Thanks,
Sayan