Abstract
One of the objectives of ensemble techniques is to improve model accuracy by driving down the variance without affecting too much the bias. In this note, we consider bagging trees. Bagging trees is an ensemble technique which consists in combining several regression trees fitted on different bootstrap samples of the training set. We demonstrate that bagging trees performs better than one of its constituent trees in the sense of the expected generalization error. Moreover, we show through an example that bagging trees outperforms not only one of its constituent tree but also the best decision tree built on the entire training set.
Keywords: Bagging trees, regression tree, generalization error, Poisson deviance loss.
Sector: Insurance
Authors: Candy Mahirwe,
Michel Denuit and Julien Trufin
Publisher: Detralytics
Date: November 2020
Language: English
Pages: 25
Reference : Detra Note 2020-4
About the authors
Candy Mahirwe
Candy is an Expert at Detralytics. During her various missions, Candy has worked on IAS19 valuation of pension plans in a consultancy firm; on the creation of an internal note about the analysis of spreads on loans of an insurance company; and as a life product manager in the actuarial department of an insurance company. Prior to joining Detralytics, Candy worked as an intern at AG Insurance.
Michel Denuit
Michel is an Honorary Scientific Advisor at Detralytics, as well as a professor in actuarial science at the Université Catholique de Louvain. He has international experience as a visiting professor, and has promoted many projects in collaboration with the industry. At Detralytics, Michel coaches young talents, provides cutting-edge training, fosters innovation and oversees R&D projects.
Julien Trufin
Julien is a Scientific Advisor at Detralytics, as well as a professor in Actuarial Science at the department of mathematics of the Université Libre de Bruxelles. Julien has experience as a consultant and a strong academic background developed at prominent institutions, including Université Laval (Canada), UCL, and ULB (Belgium). At Detralytics, Julien coaches young talents, provide cutting-edge training, fosters innovation and oversees R&D projects.
