{"id":4068,"date":"2020-11-01T11:56:39","date_gmt":"2020-11-01T11:56:39","guid":{"rendered":"https:\/\/detralytics.com\/?p=4068"},"modified":"2026-06-22T14:50:21","modified_gmt":"2026-06-22T14:50:21","slug":"the-reason-why-bagging-trees-outperform-decision-tree","status":"publish","type":"post","link":"https:\/\/detralytics.com\/en\/new\/the-reason-why-bagging-trees-outperform-decision-tree\/","title":{"rendered":"The reason why bagging trees outperform decision tree…"},"content":{"rendered":"\t\t
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The reason why bagging trees outperform decision tree<\/h1>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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DetraNote 2020-4<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Abstract<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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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.<\/p>

Keywords: <\/span><\/strong>Bagging trees, regression tree, generalization error, Poisson deviance loss.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Sector: <\/b>Insurance<\/b><\/p>\n

Authors:<\/b> Candy Mahirwe,<\/p>\n

Michel Denuit and Julien Trufin<\/p>\n

Publisher:<\/b> Detralytics<\/p>\n

Date:<\/b> November 2020<\/p>\n

Language:<\/b> English<\/p>\n

Pages:<\/b> 25<\/p>\n

Reference :<\/b> Detra Note 2020-4<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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About the authors<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Candy Mahirwe<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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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.\u00a0<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"Michel\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Michel Denuit<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Michel is a\u00a0Scientific Advisor at Detralytics, as well as a professor in actuarial science at the Universit\u00e9 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.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"Julien\"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Julien Trufin<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Julien is a Scientific Advisor at Detralytics, as well as a professor in Actuarial Science at the department of mathematics of the Universit\u00e9 Libre de Bruxelles. Julien has experience as a consultant and a strong academic background developed at prominent institutions, including Universit\u00e9 Laval (Canada), UCL, and ULB (Belgium). At Detralytics, Julien coaches young talents, provide cutting-edge training, fosters innovation and oversees R&D projects.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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We demonstrate that bagging trees performs better than one of its constituent trees in the sense of the expected generalization error.<\/p>\n","protected":false},"author":1,"featured_media":1755,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[138,1,133],"tags":[131],"class_list":["post-4068","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-138","category-new","category-publications","tag-detranote"],"yoast_head":"\nDetra Note : why bagging trees outperform decision tree.<\/title>\n<meta name=\"description\" content=\"We demonstrate that bagging trees performs better than one of its constituent trees in the sense of the expected generalization error.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" 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