Credit Risk Modeling in R
Lore Dirick
Manager of Data Science Curriculum at Flatiron School
Ideal scenario
Gini = 2*prop(default)*prop(non-default)
Gini_R = 2*(250/500)*(250/500) = 0.5
Gini_N2 = 2*(80/230)*(150/230) = 0.4536
Gini_N1 = 2*(170/270)*(100/270) = 0.4664
Gain
= Gini_R-prop(cases in N1)*Gini_N1 - prop(cases in N2) * Gini_N2
= 0.5 - 270/500 * 0.4664 - 230/500 * 0.4536
= 0.039488
Credit Risk Modeling in R