Consider the training examples shown in the following table for binary classification (class labels are mammals and non-mammals): Give Birth yes no no yes no no yes no yes yes no no yes no no no no no yes no Can Fly no no no no no no yes yes no no no no no no no no no yes no Live in Water no no yes yes sometimes no no no no yes sometimes sometimes no yes sometimes no no no yes no Have Legs yes no no no yes yes yes yes yes no yes yes yes no yes yes yes yes no Class mammals non-mammals non-mammals mammals non-mammals non-mammals mammals non-mammals mammals non-mammals non-mammals non-mammals mammals non-mammals non-mammals non-mammals mammals non-mammals mammals non-mammals yes yes Table credit: Introduction to Data Mining (2nd Edition), by Tan, Steinbach, Karpatne, Kumar a. (15%) Compute the misclassification error rate for the entire training set and each attribute (i.e., Give Birth, Can Fly, Live in Water, Have Legs) b. (5%) Using misclassification error rate, which attribute is better to be used for splitting (to build a decision tree) and why? c. (15%) Compute the Gini index for the entire training set and each attribute (i.e., Give Birth, Can Fly, Live in Water, Have Legs) d. (5%) Using Gini index, which attribute is better to be used for splitting (to build a decision tree) and why? e. (40%) Build a decision tree with a depth of no more than 2 using the greedy approach and the Gini index as the splitting criterion. f. (20%) Based on the decision tree built in part (e), compute the confusion matrix, accuracy, precision, recall, and F1 for the training set.