Regularizing separate terms in 2d logistic regression Exercise 8.7 Source: Jaaakkola.) a. Consider the data in Figure 8.13, where we fit the model p(y-11x,w) = (wow121W212). Suppose we fit the model by maximum likelihood, i.e., we minimize J(w)w, Dtrain) (8.133) where (w, Dtrain) is the log likelihood on the training set. Sketch a possible decision boundary corresponding to w. (Copy the figure first (a rough sketch is enough), and then superimpose your answer on your copy, since you will need multiple versions of this figure). Is your answer (decision boundary) unique? How many classification errors does your method make on the training set? b. Now suppose we regularize only the wo parameter, i.e., we minimize (8.134) Suppose is a very large number, so we regularize uo all the way to 0, but all other parameters are unregularized. Sketch a possible decision boundary. How many classification errors does your method make on the training set? Hint: consider the behavior of simple linear regression, wo + W111WyT2 c. Now suppose we heavily regularize only the wi parameter, i.e., we minimize (8.135) Sketch a possible decision boundary. How many classification errors does your method make on the training set? 8.6. Generattve vs discriminattve classifiers 279 x2 x1 Figure 8.13 Data for logistic regression question. d. Now suppose we heavily regularize only the w2 parameter. Sketch a possible decision boundary. How many classification errors does your method make on the training set