Combination of unsupervised discretization methods for credit risk
José G. Fuentes Cabrera,
Hugo A. Pérez Vicente,
Sebastián Maldonado
et al.
Abstract:Creating robust and explainable statistical learning models is essential in credit risk management. For this purpose, equally spaced or frequent discretization is the de facto choice when building predictive models. The methods above have limitations, given that when the discretization procedure is constrained, the underlying patterns are lost. This study introduces an innovative approach by combining traditional discretization techniques with clustering-based discretization, specifically k means and Gaussian … Show more
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