Pesantez-Narvaez, J.; Guillen, M.; Alcañiz, M. Predicting Motor Insurance Claims Using Telematics Data—XGBoost versus Logistic Regression. Risks2019, 7, 70.
Pesantez-Narvaez, J.; Guillen, M.; Alcañiz, M. Predicting Motor Insurance Claims Using Telematics Data—XGBoost versus Logistic Regression. Risks 2019, 7, 70.
Pesantez-Narvaez, J.; Guillen, M.; Alcañiz, M. Predicting Motor Insurance Claims Using Telematics Data—XGBoost versus Logistic Regression. Risks2019, 7, 70.
Pesantez-Narvaez, J.; Guillen, M.; Alcañiz, M. Predicting Motor Insurance Claims Using Telematics Data—XGBoost versus Logistic Regression. Risks 2019, 7, 70.
Abstract
XGBoost is recognized as an algorithm with exceptional predictive capacity. Models for a binary response indicating the existence of accident claims vs. no claims can be used to identify the determinants of traffic accidents. We compare the relative performances of logistic regression and XGBoost approaches for predicting the existence of accident claims using telematics data. The dataset contains information from an insurance company about individuals’ driving patterns – including total annual distance driven and percentage of total distance driven in urban areas. Our findings show that logistic regression is a suitable model given its interpretability and good predictive capacity. XGBoost requires numerous model-tuning procedures to match the predictive performance of the logistic regression model and greater effort as regards interpretation.
Keywords
dichotomous response; predictive model; tree boosting; GLM; machine learning
Subject
Business, Economics and Management, Econometrics and Statistics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
