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• W3184200411 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 in the spirit of Shapley values and integrated gradients." @default.
• W3184200411 created "2021-08-02" @default.
• W3184200411 creator A5004533209 @default.
• W3184200411 creator A5048936137 @default.
• W3184200411 date "2021-07-23" @default.
• W3184200411 modified "2023-09-23" @default.
• W3184200411 title "LocalGLMnet: interpretable deep learning for tabular data" @default.
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• W3184200411 hasPublicationYear "2021" @default.
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