LocalGLMnet: interpretable deep learning for tabular data
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References listed on IDEAS
- Mukund Sundararajan & Amir Najmi, 2019. "The many Shapley values for model explanation," Papers 1908.08474, arXiv.org, revised Feb 2020.
- Kevin Kuo & Ronald Richman, 2021. "Embeddings and Attention in Predictive Modeling," Papers 2104.03545, arXiv.org.
- 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.
- 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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- 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.
- 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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NEP fields
This paper has been announced in the following NEP Reports:- NEP-BIG-2021-08-16 (Big Data)
- NEP-CMP-2021-08-16 (Computational Economics)
- NEP-ECM-2021-08-16 (Econometrics)
- NEP-ISF-2021-08-16 (Islamic Finance)
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