Question 4 - Decision Tree Learning (20%) Consider the following database of stars represented by 5 training examples. The target attribute is 'Red dwarf', which can have values 'yes' or 'no'. This is to be predicted based on the other attributes of the star. Note that Radius refers to the relative radius based on the Sun of our solar system and the temperature is measured in Kelvin. Star Temperature Radius Colour Red dwarf 1 3650 1324 Red No 2 8930 0.0095 White No 3 3511 0.109 Red Yes 4 3570 1480 Red No 5 2840 0.11 Red Yes log,(x) = logo (x) logio (2) 1. Calculate the entropy of the target attribute [Note: ] 2. Construct the decision tree structure from the above examples, which would be learned by the ID3 algorithm. 3. Show the value of the information gain for each candidate attribute at each step in the construction of the tree. The deliverables should be the calculations or tree structure for each of the above questions. Entropy(s) = Ep los:(1) - EP -p, log, (p) For quick access, the definition of entropy is: where p, is the proportion of S belonging to class