 LocalGLMnet: interpretable deep learning for tabular dataRonald Richman and Mario V. Wüthrich Scandinavian Actuarial Journal, 2023, vol. 2023, issue 1, 71-95 Abstract: Deep learning models have gained great popularity in statistical modeling because they lead to very competitive regression models, often outperforming classical statistical models such as generalized linear models. The disadvantage of deep learning models is that their solutions are difficult to interpret and explain, and variable selection is not easily possible because deep learning models solve feature engineering and variable selection internally in a nontransparent way. Inspired by the appealing structure of generalized linear models, we propose a new network architecture that shares similar features as generalized linear models but provides superior predictive power benefiting from the art of representation learning. This new architecture allows for variable selection of tabular data and for interpretation of the calibrated deep learning model, in fact, our approach provides an additive decomposition that can be related to other model interpretability techniques. Date: 2023 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:taf:sactxx:v:2023:y:2023:i:1:p:71-95 Ordering information: This journal article can be ordered from DOI: 10.1080/03461238.2022.2081816 Access Statistics for this article Scandinavian Actuarial Journal is currently edited by Boualem Djehiche More articles in Scandinavian Actuarial Journal from Taylor & Francis Journals |
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