Question 5. (20 pts.) The ID3 algorithm for learning a decision tree for a given set of training examples along with their measured attributes (features) and target labels classifier is given below. - ID3(S, Attributes, Label) If all examples are labeled the same return a single node tree with Label Otherwise Begin A= attribute in Attributes that best classifies S (Create a Root node for tree) for each possible value v of A Add a new tree branch corresponding to A=v Let Sv be the subset of examples in S with A=v if Sv is empty: add leaf node with the common value of Label in S Else: below this branch add the subtree ID3(Sv, Attributes - \{a\}, Label) End Return Root How is the "best" in the statement "...that best classifies S " defined in the ID3 algorithm? Explain in enough level-of-detail