Analysis Accuracy of XGBoost Model for Multiclass Classification - A Case Study of Applicant Level Risk Prediction for Life Insurance

Widya Fajar Mustika, Hendri Murfi, Yekti Widyaningsih

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Citations (Scopus)

Abstract

Risk level assessment for insurance applicants is an important part of life insurance, so it needs to be classified. Determination of the level of risk claims on life insurance is based on the applicant's historical data. Submission to become a member of a life insurance requires a short time. But the application of a machine learning model can help classify prospective insurance applicants based on the level of risk quickly. One machine learning model is Extreme Gradient tree boosting (XGBoost) which is a decision tree based model. This model is used to predict risk in life insurance. The missing values in the data used are overcome by several strategies in the data processing process to increase the accuracy value of the XGBoost model. The results of this study show that the accuracy of the XGBoost model is 0.60730 with kappa units which indicates that the XGBoost model is very good and can be applied to the problem of predicting the level of risk claims for life insurance applicants. When compared to the decision tree, random forest and Bayesian ridge models, the performance of the XGoost model still excels in processing missing values in the data used.

Original languageEnglish
Title of host publicationProceeding - 2019 5th International Conference on Science in Information Technology
Subtitle of host publicationEmbracing Industry 4.0: Towards Innovation in Cyber Physical System, ICSITech 2019
EditorsAwang Hendrianto Pratomo, Andri Pranolo, Leonel Hernandez, Rafal Drezewski, Roman Voliansky, Mohamad Shanudin Zakaria, Bagus Muhammad Akbar, Shoffan Saifullah, Ahmad Taufiq Akbar, Rochmat Husaini, Heriyanto Heriyanto, Andiko Putro Suryotomo, Vynska Amalia Permadi, Sylvert Prian Tahalea
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages71-77
Number of pages7
ISBN (Electronic)9781728123806
DOIs
Publication statusPublished - Oct 2019
Event5th International Conference on Science in Information Technology, ICSITech 2019 - Yogyakarta, Indonesia
Duration: 23 Oct 201924 Oct 2019

Publication series

NameProceeding - 2019 5th International Conference on Science in Information Technology: Embracing Industry 4.0: Towards Innovation in Cyber Physical System, ICSITech 2019

Conference

Conference5th International Conference on Science in Information Technology, ICSITech 2019
Country/TerritoryIndonesia
CityYogyakarta
Period23/10/1924/10/19

Keywords

  • big data
  • ensemble learning
  • machine learning
  • multi-class classification
  • risk level prediction
  • XGBoost

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