  Deep learning models have gained great popularity in statistical modeling
because they lead to very competitive regression models, often outperforming
classical statistical models such as generalized linear models. The
disadvantage of deep learning models is that their solutions are difficult to
interpret and explain, and variable selection is not easily possible because
deep learning models solve feature engineering and variable selection
internally in a nontransparent way. Inspired by the appealing structure of
generalized linear models, we propose a new network architecture that shares
similar features as generalized linear models, but provides superior predictive
power benefiting from the art of representation learning. This new architecture
allows for variable selection of tabular data and for interpretation of the
calibrated deep learning model, in fact, our approach provides an additive
decomposition in the spirit of Shapley values and integrated gradients.
