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we used to create GINI curve using lift created with help of percentage of good and bad for scorecard modelling. But what I have studied that ROC curve is created using Confusion matrix with Specificity (1- True Negative) as x axis and sensitivity( true positive) as Y axis.

So the result of GINI and ROC is same with one difference is that latter also take into consideration of concordance and discordance value ( TP, FP, FN, TN).

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The Gini Coefficient is the summary statistic of the Cumulative Accuracy Profile (CAP) chart. It is calculated as the quotient of the area which the CAP curve and diagonal enclose and the corresponding area in an ideal rating procedure.

Area Under Receiver Operating Characteristic curve (or AUROC for short) is the summary statistic of the ROC curve chart.

The direct conversion between Gini and AUROC is given by: $$ Gini = 2\times AUROC - 1$$

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    $\begingroup$ This is a bit short for an answer, could you try to sumarize the conclusions from the linked paper? $\endgroup$
    – kjetil b halvorsen ♦
    Commented Jul 31, 2017 at 13:21
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    $\begingroup$ I'll do it. This is my research topic for this week. $\endgroup$
    – the_owl
    Commented Jul 31, 2017 at 13:30

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