Consider the decision tree shown below: a. Compute the generalization error rate of the tree using the optimistic approach. b. Compute the generalization error rate of the tree using the pessimistic approach. (For simplicity, use the strategy of adding a factor of 0.5 to each leaf node.) c. Compute the generalization error rate of the tree using the validation set shown above. This approach is known as reduced error pruning. Training: Instance A B C Class 1 0 0 0 + 2 0 0 1 + 3 0 1 0 + 4 0 1 1 - 5 1 0 0 + 6 1 0 0 + 7 1 1 0 - 8 1 0 1 + 9 1 1 0 + 10 1 1 0 + Validation: Instance A B C Class 11 0 0 0 + 12 0 1 1 + 13 1 1 0 + 14 1 0 1 - 15 1 0 0 +