Question 5. 25 Marks A. Assume that the support vector machine (SVM) classifier is applied on a given dataset and the output from the classifier benchmarked against the actual labels of the dataset is depicted in the following table: Actual Label Y Y Y N N N IN TY NNN SVM Output Y YN YN YN Y Y Y Y a) Provide a general overview of the confusion matrix in a tabular form showing the true positives (TP), true negatives (TN), false positives (FP) and false negatives (FN). [2 marks b) Create a confusion matrix in a tabular form for the output from the classifier and the actual dataset labels. 16 marks c) From the confusion matrix in (b), compute the following performance measures by showing the step-by-step procedure involved in arriving at your results. i. Accuracy [2 marks] ii. Precision [2 marks] Examiner: Dr Solomon Mensah Page 5 of 21 iii. Recall iv. F-measure [2 marks] [2 marks] B. Consider the following set of one-dimensional points: {6, 12, 18, 24, 30, 42, 48). a) For each of the following sets of initial centroids i. {18.45) ii. 15.40) create two clusters by assigning each point to the nearest centroid, and then calculate the sum squared error for each set of two clusters after updating the centroids. 16 marks b) Do both sets of centroids in B(a) represent stable solutions, i.e., if the K-means algorithm is applied to this set of points using the given centroids as the starting centroids, would there be any change in the clusters generated? [3 marks