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?


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.

Source : Link , Question Author : xboard , Answer Author : Miguel

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