Gini index for binary variables in data mining
What is Gini index?
Gini index is the most commonly used measure of inequality. Also referred as Gini ratio or Gini coefficient.
Gini index for binary variables is calculated in the example below.
Student | inHostel | Target Class |
Yes | True | Yes |
Yes | True | Yes |
Yes | False | No |
False | False | Yes |
False | True | No |
False | True | No |
False | False | No |
True | False | Yes |
False | True | No |
Now we will calculate Gini index of student and inHostel.
Step 1:
Gini(X) = 1 – [(4/9)2 + (5/9)2] = 40/81
Step 2:
Gini(Student = False) = 1 – [(1/5)2 + (4/5)2] = 8/25
Gini(Student = True) = 1 – [(3/4)2 + (1/4)2] = 3/8
GiniGain(Student) = Gini(X) – [4/9· Gini(Student = True) + 5/9· Gini(Student = False)] = 0.149
Step 3:
Gini(inHostel = False) = 1 – [(2/4)2 + (2/4)2] = 1/2
Gini(inHostel = True) = 1 – [(2/5)2 + (3/5)2] = 12/25
GiniGain(inHostel) = Gini(X) – [5/9· Gini(inHostel = True) + 4/9· Gini(inHostel = False)] = 0.005
Results
Best split point is Student because it has high gini gain.
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