LASSO Regularization within the LocalGLMnet Architecture
29 Pages Posted: 24 Sep 2021 Last revised: 1 Jun 2022
Date Written: September 20, 2021
Deep learning models have been very successful in the application of machine learning methods, often out-performing classical statistical models such as linear regression models or generalized linear models. On the other hand, deep learning models are often criticized for not being explainable nor allowing for variable selection. There are two different ways of dealing with this problem, either we use post-hoc model interpretability methods or we design specific deep learning architectures that allow for (more) easy interpretation and explanation.
This paper builds on our previous work on the LocalGLMnet architecture that gives an interpretable deep learning architecture. In the present paper, we show how group LASSO regularization (and other regularization schemes) can be implemented within the LocalGLMnet architecture so that we receive feature sparsity for variable selection. We benchmark our approach with the recently developed LassoNet of Lemhadri et al.
Keywords: Deep learning, neural networks, LocalGLMnet, regression model, variable selection, regularization, LASSO, group LASSO, ridge regularization, Tikhonov regularization.
JEL Classification: C14
Suggested Citation: Suggested Citation