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Halim, Giselle
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Comparing machine learning and binary regression approach for motor insurance prediction Sefina Samosir, Ridha; Bazán Guzmán, Jorge Luis; Halim, Giselle
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 3: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i3.pp1576-1585

Abstract

This study compares the performance of binary regression with the power cauchit (PC) link function and random forest in predicting motor insurance policyholder behavior using an imbalanced dataset. The dataset comprises 4,000 policyholders, with the response variable indicating whether a client purchased a full coverage plan (1) or not (0). Predictors include characteristics such as men, urban, private, age, and seniority. Binary regression was implemented using PyStan, while random forest was created with scikit-learn without additional hyperparameter tuning. Results demonstrate that random forest outperformed binary regression in a range of performance metrics, as well as specialized metrics suitable for imbalanced data. Findings point to the effectiveness of machine learning (ML) algorithms, exemplified by random forest, offer more robust performance in handling complex, imbalanced datasets compared to traditional statistical models. This highlights the potential of random forest to improve predictive accuracy in applications such as motor insurance policyholder behavior analysis.
Co-Authors Bazán Guzmán, Jorge Luis Sefina Samosir, Ridha