deepregression: a Flexible Neural Network Framework for Semi-Structured Deep Distributional Regression
This framework addresses the need for scalable and interpretable statistical modeling in machine learning by bridging deep learning and classical statistics, though it is incremental as it builds on existing methods.
The paper introduces deepregression, a flexible neural network framework for semi-structured deep distributional regression that combines additive regression models and deep networks to learn conditional distributions, achieving state-of-the-art predictive performance while maintaining interpretability.
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.