A credit risk team builds a logistic regression model and reports Gini = 0.62. How is the Gini coefficient derived from the AUC?
Select an answer to reveal the explanation.
Short Explanation and Infographic
Here's the deal — b is correct because Gini = 2 AUC - 1. With AUC = 0.81, Gini = 2(0.81) - 1 = 0.62. Gini normalizes AUC to a -1 to +1 scale where 0 = random, 1 = perfect discrimination, -1 = perfectly reversed. A is wrong (the formula is 2AUC-1, not AUC-0.5). C is wrong; 1-AUC would give a…
Full explanation below image
Full Explanation
B is correct because Gini = 2 AUC - 1. With AUC = 0.81, Gini = 2(0.81) - 1 = 0.62. Gini normalizes AUC to a -1 to +1 scale where 0 = random, 1 = perfect discrimination, -1 = perfectly reversed. A is wrong (the formula is 2AUC-1, not AUC-0.5). C is wrong; 1-AUC would give a negative Gini for good models. D is wrong; squaring AUC has no standard interpretation.