# Why use Normalized Gini Score instead of AUC as evaluation?

Kaggle's competition Porto Seguro's Safe Driver Prediction uses Normalized Gini Score as evaluation metric and this got me curious about the reasons for this choice. What are the advantages of using normalized gini score instead of the most usual metrics, like AUC, for evaluation?

• The Kaggle website used to have this answer: "There is a maximum achievable area for a "perfect" model since not all of the positive examples occur immediately. We use the normalized Gini coefficient by dividing the Gini coefficient of your model by the Gini coefficient of the perfect model." but it is not available anymore. webcache.googleusercontent.com/… Commented Oct 10, 2017 at 1:01
• So, gini is just auc on a different scale. Or are auc and gini applied to different curves? That is not clear to me as non-expert in machine-learning. The question is not very clear about this. Commented Oct 10, 2017 at 1:10

I believe that the Gini score is merely a reformulation of the AUC: $$gini = 2 \times AUC -1$$ As for why use this instead of the commonly used AUC, the only reason I can think of is that a random prediction will yield a Gini score of 0 as opposed to the AUC which will be 0.5.