Handling Missing Data while constructing Decision Trees. We assume that data is given as some feature e Rd, in which some attributes/entries are unknown. For this problem, we will use the train dataset D with 5 datapoints, binary label, and d = 3 attributes as given in Table 4. Example 21 22 23 Label y 1 1 0 0 0 2 1 0 1 0 3 0 1 0 0 4 1 1 1 1 5 1 1 1 0 Table 1: Dataset for Problem 4 - Decision Tree by Hand. (a) Learn a decision tree for the given dataset D using Information gain as the splitting criterion