8- You have implemented a logistic regression model for binary classification P(Y=yr, w)=(y(w+b)) = 1+ exp(-y(wx+b)) (6) where the sigmoid function has the shape on the left figure 0.5 0 2 4 You decide to train your model using gradient descent. However, before you can turn your assignment in, your friends stop by to give you some suggestions. (a) Lennie sees your regressor implementation and says there's a much simpler way! Just leave out sigmoids, and let y=wr+b. The derivatives are a lot less hassle and it runs faster. What's wrong with Lennie's approach? (b) George comes in and says that your logistic regressor is too complicated, but for a different reason. The sigmoids are confusing and basically the same answer would be computed if we used sign functions, on the right figure above, instead of the sigmoid. What's wrong with George's approach?