{"id":3777,"date":"2023-09-21T13:15:46","date_gmt":"2023-09-21T13:15:46","guid":{"rendered":"https:\/\/detralytics.com\/?p=3777"},"modified":"2026-06-22T14:33:22","modified_gmt":"2026-06-22T14:33:22","slug":"distill-knowledge-of-additive-tree-models-into-generalized-linear-models","status":"publish","type":"post","link":"https:\/\/detralytics.com\/en\/publications\/distill-knowledge-of-additive-tree-models-into-generalized-linear-models\/","title":{"rendered":"Distill knowledge of additive tree models into generalized linear models"},"content":{"rendered":"\t\t
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Distill knowledge of additive tree models into generalized linear models<\/h1>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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DetraNote 2023-6<\/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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Generalized additive models (GAMs) are a leading model class for interpretable machine learning. GAMs were originally trained using smoothing splines. Recently, tree-based GAMs where shape functions are gradient-boosted ensembles of bagged trees were proposed (e.g. Explainable Boosting Machine). In this paper, we introduce a competing three-step GAM learning approach where we combine i) the knowledge of the way to split the covariates space brought by an Additive tree model (ATM), ii) an ensemble of predictive linear scores derived from Generalized linear models (GLMs) using a binning strategy based on the ATM, iii) a final GLM to have a prediction model that ensures auto-calibration. Numerical experiments illustrate the very good performances of our approach on several datasets compared to GAM with splines, EBM or GLM with binarsity penalization. A case-study in trade credit insurance is also provided.<\/p>

Keywords: <\/span><\/strong>Additive tree ensembles, Auto-calibration, Generalized additive models, Generalized linear models, Partitioning methods, XAI.<\/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
Expertise: <\/b>Machine learning
<\/b>Authors:<\/b> Arthur Maillart,
Christian Y. Robert
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Publisher:<\/b> Detralytics
Date:<\/b> September 2023
Language:<\/b> English
Pages:<\/b> 22
Reference :<\/b> Detra Note 2023-6<\/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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\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"Rond_Arthur\"\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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Arthur Maillart<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Arthur is a Senior Expert and Innovation Lead at Detralytics. Detralytics\u2019 Innovation Lab supports innovation of the insurance market players and contributes to actuarial science by co-constructing cutting-edge projects with other entities, organising training courses and sharing our knowledge through our publications and open source tools.<\/p>

Since becoming a PhD in 2021, his missions naturally focus on non-life insurance and machine learning, for which he continues to develop his skills in Python, Git & Linux. Over the past two years, he has built a complete monthly payment calculation tool for a mortgage broker and a credit insurance pricing tool.\u200b<\/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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Christian Y. Robert<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Christian is the Director of ISFA in Lyon (FR) and a professor in Actuarial Science & Statistics. He has experience as the Research Director of the Laboratory of Finance and Actuarial Science at ISFA (Lyon).<\/p>

Christian is an Honorary Member of the Institut des Actuaires (Paris). He has published more than 50 scientific papers.<\/p>

At Detralytics, Christian 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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Generalized additive models (GAMs) are a leading model class for interpretable machine learning. GAMs were originally trained using smoothing splines.<\/p>\n","protected":false},"author":1,"featured_media":4253,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[135,133],"tags":[131],"class_list":["post-3777","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-135","category-publications","tag-detranote"],"yoast_head":"\nDetra Note : additive tree models into generalized linear models<\/title>\n<meta name=\"description\" content=\"Generalized additive models are a leading model class for interpretable machine learning. 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