Draw a decision tree for this scenario by using algorithm LEARN$-$DECISION$-$TREE shown in fig $19\mathrm{.}5$ of your textbook. Use appropriate atributes and decision nodes to represent each step of the decision$-$making process based on the concept of entropy, remainder and information gain as discussed in section $19\mathrm{.}3\mathrm{.}3$ on page $661-662$ og the textbook. $(10$ points$)$

function LEARN$-$DECISION$-$TREE$($examples$,$ attributes, parent$\_$examples$)$ returns a tree

Figure $19\mathrm{.}5$ The decision tree learning algorithm. The function IMPORTANCE is described

in Section $19\mathrm{.}3\mathrm{.}3.$ The function PLURALITY$-$VALUE selects the most common output value

among a set of examples, breaking ties randomly.