Often in binary classification we are interested in the differences in the output of our current classifier, g$,$ and an unknown function 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$,$ and let f$,$ g : $\{1,1\}$n $\{1,1\}$ be two Boolean functions.$($a$)[6$ points$]$ Prove thatPxD$[$f$($x$)=$ g$($x$)]=1$ ExD$[$f$($x$)$g$($x$)].2($b$)[4$ points$]$ Would this still be true if the domain were some other domain $($such as Rn$,$ where R denotes the real numbers, with say the Gaussian distribution$)$ instead of $\{1,1\}$n$?$ If yes, justify your answer. If not, give a counterexample.Note: Only the domain changes here. The output is still boolean.