A comparison of the performance of alternative Machine Learning algorithms on a credit risk dataset

10 Pages Posted: 9 Mar 2021

See all articles by Roberto Torresetti

Roberto Torresetti

Università degli Studi di Milano; Intesa SanPaolo

Date Written: March 7, 2021


We compare alternative methodologies for Credit Risk estimation. Here we introduce a Bayesian Model Averaging (BMA) of Logistic Regressions for Probability of Default modeling where the model space is sampled via Markov Chain Monte Carlo methods making use of the approximation of the posterior probability introduced in [Schwarz, 1978] and used in the context of sparse problems in [Sala-i Martin et al., 2004], [Gross and Poblacion, 2004] and [Torresetti, 2021]. We will compare this methodology against some popular Machine Learning ensemble methods for regression trees: Bagging (Bootstrap Aggregating) and Boosting (Adaptive Boosting, Gradient Boosting). The results on the credit risk dataset at our disposal show how Gradient Boosting proves to be the top performer but with a slow convergence speed given the cooling coecient that was used to make it less greedy. As not too distant seconds we see the performance of BMA and Adaptive Boosting where in particular BMA proves to have a faster convergence speed among the specific versions of the algorithms implemented. Finally a simple average of estimates as in Bagging proves to have the lowest performance of the ensemble methods considered.

Keywords: Credit Risk, Probability of Default, PD, Machine Learning, Bayesian Model Average, Markov Chain Monte Carlo, MCMC, Regression Trees, Random Forest, Ensemble Learning, AdaBoost, Adaptive Boosting, GrdBoost, Gradient Boosting, Bootstrap Aggregating, Bagging.

JEL Classification: C11, C13, C15, C51, C52.

Suggested Citation

Torresetti, Roberto, A comparison of the performance of alternative Machine Learning algorithms on a credit risk dataset (March 7, 2021). Available at SSRN: https://ssrn.com/abstract=3799452 or http://dx.doi.org/10.2139/ssrn.3799452

Roberto Torresetti (Contact Author)

Università degli Studi di Milano ( email )

via Festa del Perdono, 7

Intesa SanPaolo ( email )


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