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

• Main Authors: Rügamer, David, Kolb, Chris, Fritz, Cornelius, Pfisterer, Florian, Kopper, Philipp, Bischl, Bernd, Shen, Ruolin, Bukas, Christina, Sousa, Lisa Barros de Andrade e, Thalmeier, Dominik, Baumann, Philipp, Kook, Lucas, Klein, Nadja, Müller, Christian L
• Format: Journal Article
• Language: English
• Published: 06.04.2021
• Subjects: Computer Science - Learning; Statistics - Computation; Statistics - Machine Learning
• DOI: 10.48550/arxiv.2104.02705

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.