often in binary classification we are interested in the difference in the output of our current classifier, g$,$ and an unknown fuction f that we are trying to learn. it is common in these cases to examine the quantity produced by f$($x$)$g$($x$)$ for a given input x$.$ For this problem, let D be an arbitrary distribution on the domain $\{-1,1\}^$n