Interpreting Deep Neural Networks with the Package innsight
Abstract
The R package innsight offers a general toolbox for revealing variable-wise interpretations of deep neural networks' predictions with so-called feature attribution methods. Aside from the unified and user-friendly framework, the package stands out in three ways: It is generally the first R package implementing feature attribution methods for neural networks. Secondly, it operates independently of the deep learning library allowing the interpretation of models from any R package, including keras, torch, neuralnet, and even custom models. Despite its flexibility, innsight benefits internally from the torch package's fast and efficient array calculations, which builds on LibTorch PyTorch's C++ backend without a Python dependency. Finally, it offers a variety of visualization tools for tabular, signal, image data or a combination of these. Additionally, the plots can be rendered interactively using the plotly package.
Cite
@article{arxiv.2306.10822,
title = {Interpreting Deep Neural Networks with the Package innsight},
author = {Niklas Koenen and Marvin N. Wright},
journal= {arXiv preprint arXiv:2306.10822},
year = {2025}
}