Problem 4 (5pts) Suppose you have the following training set, and fit a logistic regression classifier h(x)=g(0+1x1+2x2). Which of the following are true? Check all that apply. A: J() will be a convex function, so gradient descent should converge to the global minimum. B: Adding polynomial features (e.g., instead using h0(x)=g(0+1x1+2x2+3x12+ 4x1x2+5x22 ) could increase how well we can fit the training data. C: The positive and negative examples cannot be separated using a straight line. So, gradient descent will fail to converge. 2 D: Because the positive and negative examples cannot be separated using a straight line, linear regression will perform as well as logistic regression on this data