5. The loss function for logistic regression for binary classification is the binary cross entropy defined as J(B) = ln(1 + e*i) – Yizi N = i=1 where zi = Bo + B1X1i + B2X2i for two features X1, and X2,1 (a) What are the partial derivatives of zi with respect to Bo, B1, and B2. (b) Compute the partial derivatives of J(B) with respect to Bo, B1, and B2. You should use the chain rule of differentiation. (c) Can you find the close form expressions for the optimal parameters ßo, ?1, and ß2 by putting the derivatives of J(B) to 0? What methods can be used to optimize the loss function J(B)?