Consider the training examples shown in Table 4.1 for a binary classification problem. Table 4.1. Data set for Exercise 2.
Customer ID Gender Car Type Shirt Size Class
1 M Family Small
2 M Sports Medium
3 M Sports Medium
4 M Sports Large
5 M Sports Extra Large
6 M Sports Extra Large
7 F Sports Small
8 F Sports Small
9 F Sports Medium
10 F Luxury Large
11 M Family Large
12 M Family Extra Large
13 M Family Medium
14 M Luxury Extra Large
15 F Luxury Small
16 F Luxury Extra Large
17 F Luxury Medium
18 F Luxury Large
(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.