This is one of the most frequently asked data science interview questions for freshers in recent times. Response:  Gini Coefficient is a measure to use feature importance in a random forest algorithm. In a binary classification scenario, when we are predicting both classes, the Gini coefficient can be computed based on AUC (Area Under the Curve) value.  It measures the inequality between values of a frequency distribution.  It is computed as the following:  Gini Coeff (Gini Coefficient) = 2 * AUC – 1  Link reference:  In the above example and figure 25, Gini Coeff = A / (A+B).