Consider the following data set comprised of three binary input attributes (A1, A2, and A3) and one binary output (See attachment). Use the algorithm in Figure 18.5 (page 702) (See attachment) to learn a decision tree for these data. Show the computations made to determine the attribute to split at each node.

function DECISION-TREE-LEARNING(eramples, attributes, parent.czamples) return a tree if ezamples is empty then return PLURALITY-VALUE(parent-eramples) else if all cramples have the same classification then return the classification else if attributes is empty then return PLURALITY-VALUE(ezamples) else A argmax a e attributes IMPORTANCE (a, ples) tree a new decision tree with root test A for each value va of Ado eas He eeeramples and e.A VE) subtree DECISION-TREE-LEARNING(ers, attributes A eramples) add a branch to tree with label (A n) and subtree subtree return tme