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LocalGLMnet: interpretable deep learning for tabular data

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  • Ronald Richman
  • Mario V. Wuthrich

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.

Suggested Citation

  • Ronald Richman & Mario V. Wuthrich, 2021. "LocalGLMnet: interpretable deep learning for tabular data," Papers 2107.11059, arXiv.org.
  • Handle: RePEc:arx:papers:2107.11059
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    File URL: http://arxiv.org/pdf/2107.11059
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    References listed on IDEAS

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    1. Mukund Sundararajan & Amir Najmi, 2019. "The many Shapley values for model explanation," Papers 1908.08474, arXiv.org, revised Feb 2020.
    2. Kevin Kuo & Ronald Richman, 2021. "Embeddings and Attention in Predictive Modeling," Papers 2104.03545, arXiv.org.
    3. Daniel W. Apley & Jingyu Zhu, 2020. "Visualizing the effects of predictor variables in black box supervised learning models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 82(4), pages 1059-1086, September.
    4. M. Merz & R. Richman & T. Tsanakas & M. V. Wuthrich, 2021. "Interpreting Deep Learning Models with Marginal Attribution by Conditioning on Quantiles," Papers 2103.11706, arXiv.org.
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    Cited by:

    1. Alex Jose & Angus S. Macdonald & George Tzougas & George Streftaris, 2022. "A Combined Neural Network Approach for the Prediction of Admission Rates Related to Respiratory Diseases," Risks, MDPI, vol. 10(11), pages 1-35, November.
    2. Muhammed Taher Al-Mudafer & Benjamin Avanzi & Greg Taylor & Bernard Wong, 2021. "Stochastic loss reserving with mixture density neural networks," Papers 2108.07924, arXiv.org.

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