2. Construct the decision tree structure from the above examples, which would be learned by the ID3 algorithm. For your convenience, convert the numerical values to categorical by comparing each star with the properties of the Sun: Temperature > 6000K Hotter Temperature < 6000K Cooler Radius > 1 Larger Radius < 1 Smaller.

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 Radius Colour Red dwarf 1 Temperature 3650 8930 1324 Red No 2 0.0095 White No 3 3511 0.109 Red Yes 4 3570 1480 Red No in 5 2840 0.11 Red Yes log (x) - log (x) log (2) 1. Calculate the entropy of the target attribute [Note: 1 2. Construct the decision tree structure from the above examples, which would be learned by the ID3 algorithm For your convenience, convert the numerical values to categorical by comparing each star with the properties of the Sun: Temperature > 6000K Hotter Temperature < 6000K Cooler Radius > 1 Larger Radius <1 smaller 3. show the value of information gain for each candidate attribute at step in construction tree. deliverables should be calculations or tree structure above questions.>