The Gini coefficient is an empirical measure of classification performance based on the area under an ROC curve (AUC). Attributed to the Italian statistician Corrado Gini (1884–1965), it can be calculated as 2 ⋅ {AUC} − 1 and thus takes values in the interval [−1, 1], where 1 indicates perfect ranking performance and − 1 indicates that all negatives are ranked before all positives. See ROC Analysis.
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(2017). Gini Coefficient. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning and Data Mining. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7687-1_343
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