17.2 Nearest-Neighbor Learning and Decision Trees 567 Exercise 17.2.2. What is the accuracy of IBk (given in the Classifier Output box)? Run IBk again, but increase the number of neighboring instances to k = 5 by entering this value in the KNN field. Here and throughout this section, continue to use cross-validation as the evaluation method. Exercise 17.2.3. What is the accuracy of IBk with five neighboring instances (k = 5)? AttributeSelection Now we investigate which subset of attributes produces the best cross-validated classification accuracy for the IBk algorithm on the glass dataset. Weka contains automated attribute selection facilities, which are examined in a later section, but it is instructive to do this manually. Performing an exhaustive search over all possible subsets of the attributes is infeasible (why?), so we apply the backward elimination procedure described in Section 7.1 (page 311). To do this, first consider dropping each attribute individually from the full dataset, and run a cross-validation for each reduced version. Once you