Suppose you have the following training set, and fit a logistic regression classifier : ho(x) = g(00+011+0x2) O O O Which of the following are true? Check all that apply. a) Adding polynomial features e.g., ho(x) = g(00+011+0x2 +03x +04x1x2 +05.x) could increase how well we can fit the training data. b) At the optimal value of 0 (e.g., found by fminunc), we will have J(0) 0. c) Adding polynomial features e.g.,he(x) = g(00+0x1 +02x2 +03x +04x1x2 +05x) x 1 2 3 would increase J(0) because we are now summing over more terms. d) If we train gradient descent for enough iterations, for some examples () in the training set it is possible to obtain he(()) > 1 X2 0.5 1.5 1 1 y 0 0 0