2 Classifier evaluation (3P) Given a d-dimensional labeled dataset D= {(21, y), (12, y2),..., (In, Yn)}, I; E Rd, Yi {C1,C2,C3}, where C1, C2, C3 are the class labels. The following table shows the classification results K(:) of the classifier K as well as the true labels yi. 2 1 2 3 4 5 6 7 8 9 10 11 12 13 K(1) C C2 C C C2 C C3 C C2 C2 C3 C3 C3 Yi C C2 1 C C2 C3 C2 C2 C1 C C3 C C3 (1) Give the confusion matrix. (As a multi-class classification problem, there is a row and a column for each class in the confusion matrix, i.e., 3 x 3) (2) Calculate the accuracy and error rate for the overall classification result, write all calculation steps. (3) For each class C; {C1,C2,C3}, calculate the precision, recall and the F1 score, write all calculation steps