deepregression: a Flexible Neural Network Framework for Semi-Structured Deep Distributional Regression

• URL: http://arxiv.org/abs/2104.02705v1
• Date: Tue, 6 Apr 2021 17:56:31 GMT
• Title: deepregression: a Flexible Neural Network Framework for Semi-Structured Deep Distributional Regression
• Authors: David R\"ugamer, Ruolin Shen, Christina Bukas, Lisa Barros de Andrade e Sousa, Dominik Thalmeier, Nadja Klein, Chris Kolb, Florian Pfisterer, Philipp Kopper, Bernd Bischl, Christian L. M\"uller
• Abstract summary: deepregression is implemented in both R and Python, using the deep learning libraries and PyTorch, respectively. deepregression is implemented in both R and Python, using the deep learning libraries and PyTorch, respectively.
• Score: 1.4909973741292273
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper describes the implementation of semi-structured deep distributional regression, a flexible framework to learn distributions based on a combination of additive regression models and deep neural networks. deepregression is implemented in both R and Python, using the deep learning libraries TensorFlow and PyTorch, respectively. The implementation consists of (1) a modular neural network building system for the combination of various statistical and deep learning approaches, (2) an orthogonalization cell to allow for an interpretable combination of different subnetworks as well as (3) pre-processing steps necessary to initialize such models. The software package allows to define models in a user-friendly manner using distribution definitions via a formula environment that is inspired by classical statistical model frameworks such as mgcv. The packages' modular design and functionality provides a unique resource for rapid and reproducible prototyping of complex statistical and deep learning models while simultaneously retaining the indispensable interpretability of classical statistical models.

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