{"id":3777,"date":"2023-09-21T13:15:46","date_gmt":"2023-09-21T13:15:46","guid":{"rendered":"https:\/\/detralytics.com\/?p=3777"},"modified":"2026-08-21T13:31:10","modified_gmt":"2026-08-21T13:31:10","slug":"distill-knowledge-of-additive-tree-models-into-generalized-linear-models","status":"publish","type":"post","link":"https:\/\/detralytics.com\/fr\/publication\/distill-knowledge-of-additive-tree-models-into-generalized-linear-models\/","title":{"rendered":"Distill knowledge of additive tree models into generalized linear models"},"content":{"rendered":"
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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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Introduction<\/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>

Mots-cl\u00e9s : <\/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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Secteur : <\/b>Assurance
Expertise: <\/b>Machine learning
<\/b>Auteurs :<\/b> Arthur Maillart,
Christian Y. Robert
<\/b><\/p>

\u00c9diteur :<\/b> Detralytics
Date:<\/b> September 2023
Langue :<\/b> Anglais
Pages:<\/b> 22
R\u00e9f\u00e9rence :<\/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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\u00c0 propos des auteurs<\/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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Docteur depuis 2021, ses missions portent naturellement sur de l\u2019assurance non-vie et le machine learning pour lesquelles il continue de d\u00e9velopper ses comp\u00e9tences en Python, Git & Linux.<\/p>

Durant ces deux derni\u00e8res ann\u00e9es, il a construit un outil complet de calcul de mensualit\u00e9s pour un courtier en pr\u00eats immobiliers et un outil de tarification pour l\u2019assurance-cr\u00e9dit.<\/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 est Directeur de l\u2019ISFA \u00e0 Lyon (France) et Professeur en sciences actuarielles et statistiques. Il a \u00e9galement exerc\u00e9 la fonction de Directeur de la recherche du Laboratoire de Finance et de Sciences Actuarielles de l\u2019ISFA (Lyon).<\/p>

Membre Honoraire de l\u2019Institut des Actuaires (Paris), Christian a publi\u00e9 plus de 50 articles scientifiques.<\/p>

Christophe de Fays est Senior Expert et Domain Lead Non-Vie R\u00e9glementaire chez Detralytics. Apr\u00e8s plusieurs ann\u00e9es dans le conseil en IT, il a d\u00e9velopp\u00e9 une solide expertise en sciences actuarielles et en gestion de projets au sein de plusieurs grandes compagnies d\u2019assurance en Belgique. Il apporte une expertise de pointe aux projets de R&D et est \u00e9galement responsable de la formation continue de nos consultants chez Detralytics.<\/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>","protected":false},"author":1,"featured_media":4253,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[133],"tags":[],"class_list":["post-3777","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-publication"],"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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