The problem refers to decision trees building. According to Wikipedia 'Gini coefficient' should not be confused with 'Gini impurity'. However both measures can be used when building a decision tree - these can support our choices when splitting the set of items.1) 'Gini impurity' - it is a standard decision-tree splitting metric (see in the link above);2) 'Gini coefficient' - each splitting can be assessed based on the AUC criterion. For each splitting scenario we can build a ROC curve and compute AUC metric. According to Wikipedia AUC=(GiniCoeff+1)/2;Question is: are both these measures equivalent? On the one hand, I am informed that Gini coefficient should not be confused with Gini impurity. On the other hand, both these measures can be used in doing the same thing - assessing the quality of a decision tree split.
Gini coefficient vs Gini impurity - decision trees
data mining