BAYESIAN ADDITIVE REGRESSION TREE APPLICATION FOR PREDICTING MATERNITY RECOVERY RATE OF GROUP LONG-TERM DISABILITY INSURANCE

  • Stevanny Budiana Center for Mathematics and Society, Department of Mathematics Parahyangan Catholic University, Indonesia
  • Felivia Kusnadi Center for Mathematics and Society, Department of Mathematics Parahyangan Catholic University, Indonesia
  • Robyn Irawan Center for Mathematics and Society, Department of Mathematics Parahyangan Catholic University, Indonesia
DOI: https://doi.org/10.30598/barekengvol17iss1pp0135-0146
Keywords: Bayesian Additive Regression Tree, Maternity Recovery Rate, Prior, Sum-of-Trees

Abstract

Bayesian Additive Regression Tree (BART) is a sum-of-trees model used to approximate classification or regression cases. The main idea of this method is to use a prior distribution to keep the tree size small and a likelihood from data to get the posterior. By fixing the tree size as small as possible, the approximation of each tree would have a little effect on the posterior, which is the sum of all output from all the trees used. Bayesian additive regression tree method will be used for predicting the maternity recovery rate of group long-term disability insurance data from the Society of Actuaries (SOA). The decision tree-based models such as Gradient Boosting Machine, Random Forest, Decision Tree, and Bayesian Additive Regression Tree model are compared to find the best model by comparing mean squared error and program runtime. After comparing some models, the Bayesian Additive Regression Tree model gives the best prediction based on smaller root mean squared error values and relatively short runtime.

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Published
2023-04-15
How to Cite
[1]
S. Budiana, F. Kusnadi, and R. Irawan, “BAYESIAN ADDITIVE REGRESSION TREE APPLICATION FOR PREDICTING MATERNITY RECOVERY RATE OF GROUP LONG-TERM DISABILITY INSURANCE”, BAREKENG: J. Math. & App., vol. 17, no. 1, pp. 0135-0146, Apr. 2023.
Issue
Vol 17 No 1 (2023): BAREKENG: Journal of Mathematics and Its Applications
Section
Articles

Copyright (c) 2023 Stevanny Budiana, Felivia Kusnadi, Robyn Irawan

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