deepregression: a Flexible Neural Network Framework for Semi-Structured Deep Distributional Regression
Abstract
In this paper we describe the implementation of semi-structured deep distributional regression, a flexible framework to learn conditional distributions based on the combination of additive regression models and deep networks. Our implementation encompasses (1) a modular neural network building system based on the deep learning library \pkg{TensorFlow} for the fusion 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 set up such models. The software package allows to define models in a user-friendly manner via a formula interface that is inspired by classical statistical model frameworks such as \pkg{mgcv}. The packages' modular design and functionality provides a unique resource for both scalable estimation of complex statistical models and the combination of approaches from deep learning and statistics. This allows for state-of-the-art predictive performance while simultaneously retaining the indispensable interpretability of classical statistical models.
Cite
@article{arxiv.2104.02705,
title = {deepregression: a Flexible Neural Network Framework for Semi-Structured Deep Distributional Regression},
author = {David Rügamer and Chris Kolb and Cornelius Fritz and Florian Pfisterer and Philipp Kopper and Bernd Bischl and Ruolin Shen and Christina Bukas and Lisa Barros de Andrade e Sousa and Dominik Thalmeier and Philipp Baumann and Lucas Kook and Nadja Klein and Christian L. Müller},
journal= {arXiv preprint arXiv:2104.02705},
year = {2022}
}