Pesantez-Narvaez, JessicaGuillén, MontserratAlcañiz, Manuela2021-04-152021-04-152021-032227-7390https://hdl.handle.net/2445/176310A 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.21 p.application/pdfengcc-by (c) Pesantez-Narvaez, Jessica et al., 2021http://creativecommons.org/licenses/by/3.0/esAnàlisi de regressióTeoria de l'estimacióProcessament de dadesSistema binari (Matemàtica)Regression analysisEstimation theoryData processingBinary system (Mathematics)info:eu-repo/semantics/article7107312021-04-15info:eu-repo/semantics/openAccess