2. Consider the training examples shown in Table 4.7 for a binary classification

problem.

(a) Compute the Gini index for the overall collection of training examples.

(b) Compute the Gini index for the Customer ID attribute.

(c) Compute the Gini index for the Gender attribute.

(d) Compute the Gini index for the Car Type attribute using multiway split.

(e) Compute the Gini index for the Shirt Size attribute using multiway

split.

(f) Which attribute is better, Gender, Car Type, or Shirt Size?

(g) Explain why Customer ID should not be used as the attribute test condition

even though it has the lowest Gini.

NOTE: Use python to solve all the problems that require calculation.

Table 4.7. Data set for Exercise 2. Customer ID Gender Car Type Shirt Size Class Family Sports Sports Sports Small Medium Medium Large CO CO CO CO SportsExtra LargeCO Sports Extra LargeCO Small Small Medium Large Large CO CO C0 CO C1 Sports Sports Sports Lixlry Family FamilyExtra LargeC1l Family Luxury Extra LargeCl Luxury Luxury Luxury Luxury Luxury Luxury 10 12 13 14 15 16 17 18 19 20 Medium C1 Small Small Medium Medium Medium Large C1 C1 C1 C1 C1 C1