please refer to the pic

3. The following table summarizes a data set with three attributes A, B, C and two class labels +, - . Build a two-level decision tree. A B C 0 20 0 10 0 0 0 20 0 TF T 2 0 0 FTFI 25 0 0 (15%) According to the classification error rate, which attribute would be chosen as the first splitting attribute? For each attribute, show the contingency table (i.e., count matrix) and the gains in classification error rate (15%) Repeat for the two children of the root node. tree? (10%) How many instances are misclassified by the resulting decision a. b. C