Please explicitly/clearly show all work/explanations, you are doing research as an undergrad for Isaac Newton at Cambridge, Newton gives you a problem to solve to begin your research. The problem is assume we have a binary classification problem with the random variables Z and X that have a joint distribution Pz.x. In other words, to determine if Z and X are from the same probability distribution Pz,x or if there are actually data points which are independent of one another Pz Px. A commonly used strategy to achieve this is to find the min of the expected log loss between the prediction f(Z, X) and label Y E{-1,1}. f* e argmin Lim (f), f: Xx2R Lum(S) := E 105 (1 +exp(-Y_S(X , Z )))]. Z In detail, provide that: f*(2, 2) = px,z(2, 2) log px()pz(2) Please explicitly/clearly show all work/explanations, you are doing research as an undergrad for Isaac Newton at Cambridge, Newton gives you a problem to solve to begin your research. The problem is assume we have a binary classification problem with the random variables Z and X that have a joint distribution Pz.x. In other words, to determine if Z and X are from the same probability distribution Pz,x or if there are actually data points which are independent of one another Pz Px. A commonly used strategy to achieve this is to find the min of the expected log loss between the prediction f(Z, X) and label Y E{-1,1}. f* e argmin Lim (f), f: Xx2R Lum(S) := E 105 (1 +exp(-Y_S(X , Z )))]. Z In detail, provide that: f*(2, 2) = px,z(2, 2) log px()pz(2)