We are excited to post a new paper, covering feature selection in our explainable deep learning architecture, the LocalGLMnet. Deep learning models are often criticized for not being explainable nor allowing for variable selection. Here, we show how group LASSO regularization can be implemented within the LocalGLMnet architecture so that we receive feature sparsity for variable selection. On several examples, we find that the proposed methods can identify less important variables successfully, even on smaller datasets! The figure below shows output from the model fit to the famous bike sharing dataset, where randomly permuted variables receive zero importance after regularization.
The paper can be found here: