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

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Listed:
  • 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)

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.

Suggested Citation

  • Jessica Pesantez-Narvaez & Montserrat Guillen & Manuela Alcañiz, 2021. "RiskLogitboost Regression for Rare Events in Binary Response: An Econometric Approach," Mathematics, MDPI, vol. 9(5), pages 1-21, March.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:5:p:579-:d:513498
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    References listed on IDEAS

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    7. Jessica Pesantez-Narvaez & Montserrat Guillen & Manuela Alcañiz, 2019. "Predicting Motor Insurance Claims Using Telematics Data—XGBoost versus Logistic Regression," Risks, MDPI, vol. 7(2), pages 1-16, June.
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