Tratamiento de clases desbalanceadas con el método del cubo en problemas de credit scoring a través de la minería de datos

Abstract

This article discusses how to apply the sampling method called the “Cube Method” in credit scoring problems to improve the precision of the predictive models obtained. This method ensures an optimal balance of the samples when working with databases whose classes of the dependent variable are highly unbalanced. Using two samples of real banking data, we provide a comparative study of the best models obtained with different data mining methods when these are applied to the original databases and the balanced ones. Finally, we conclude that when the samples are balanced the predictive capacity of the classification algorithms is more precise and the models used reduce the economic cost of the classification.