Q$1-$A decision tree is a tree$-$like graph with nodes representing the place where we pick an attribute and ask a question; edges represent the answers the to the question; and the leaves represent the actual output or class label. They are used in non$-$linear decision making with simple linear decision surface. Decision trees classify the examples by sorting them down the tree from the root to some leaf node, with the leaf node providing the classification to the example. Each node in the tree acts as a test case for some attribute, and each edge descending from that node corresponds to one of the possible answers to the test case. This process is recursive in nature and is repeated for every subtree rooted at the new nodes.

A test on physical descriptive data $($size$)$ of the following Table into three partitions, namely small, medium and large.

a$)$ If the attribute height has the highest information gain, show the final decision tree returned by the algorithm.

b$)$ If the attribute gender has the highest information gain, show the final decision tree returned by the algorithm.

Table$-$ Gender, Height and Size

$\backslash$table$[[$No$,$Gender,Height,Size$],[1,K,168,$Medium$],[2,K,150,$Small$],[3,K,164,$Medium$],[4,E,166,$Medium$],[5,E,165,$Small$],[6,K,168,$Medium$],[7,E,163,$Small$],[8,E,170,$Medium$],[9,K,167,$Medium$],[10,K,175,$Large$],[11,E,170,$Medium$],[12,E,165,$Small$],[13,E,173,$Medium$],[14,E,180,$Large$],[15,E,185,$Large$]]$