Model averaging and ensemble methods for risk corporate estimation
SYstemic Risk TOmography: Signals, Measurements, Transmission Channels, and Policy Interventions
Marika Vezzoli University of Brescia
Silvia Figini University of Pavia
! In this study we investigate ensemble learning and classical model averaging in order to identify which procedure performed better in terms of predictive accuracy
! We compare ensemble learning approaches, like Random Forest (Breiman, 2001) with Bayesian Model Averaging (BMA) (e.g. Steel, 2011)
! Moreover, we compare single models with respect to their aggregated version. More precisely:
! Classification Trees vs Random Forest ! Logistic Regression vs Bayesian Model Averaging
! In order to make a coherent comparison among the models we have fixed a set of performance indicators able to assess the models at hand
! Empirical evidences are given on a real credit risk data sample
We have compared ! BMA with Random Forests
and also ! Logistic Regression vs Bayesian Model Averaging ! Classification Trees vs Random Forests
Both in the parametric and non parametric frameworks we underline that ensemble models perform better in terms of the key performance indicators employed
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