RiskLogitboost Regression for Rare Events in Binary Response: An Econometric Approach

Jessica Pesantez-Narvaez, Montserrat Guillen and Manuela Alcañiz
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Jessica Pesantez-Narvaez: Department of Econometrics, Riskcenter-IREA, Universitat de Barcelona, 08034 Barcelona, Spain
Montserrat Guillen: Department of Econometrics, Riskcenter-IREA, Universitat de Barcelona, 08034 Barcelona, Spain
Manuela Alcañiz: Department of Econometrics, Riskcenter-IREA, Universitat de Barcelona, 08034 Barcelona, Spain

Mathematics, 2021, vol. 9, issue 5, 1-21

Abstract: A boosting-based machine learning algorithm is presented to model a binary response with large imbalance, i.e., a rare event. The new method (i) reduces the prediction error of the rare class, and (ii) approximates an econometric model that allows interpretability. RiskLogitboost regression includes a weighting mechanism that oversamples or undersamples observations according to their misclassification likelihood and a generalized least squares bias correction strategy to reduce the prediction error. An illustration using a real French third-party liability motor insurance data set is presented. The results show that RiskLogitboost regression improves the rate of detection of rare events compared to some boosting-based and tree-based algorithms and some existing methods designed to treat imbalanced responses.

Keywords: boosting; accuracy; interpretation; unbiased estimates (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
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