innsight: Get the Insights of Your Neural Network

Interpretation methods for analyzing the behavior and individual predictions of modern neural networks in a three-step procedure: Converting the model, running the interpretation method, and visualizing the results. Implemented methods are, e.g., 'Connection Weights' described by Olden et al. (2004) <doi:10.1016/j.ecolmodel.2004.03.013>, layer-wise relevance propagation ('LRP') described by Bach et al. (2015) <doi:10.1371/journal.pone.0130140>, deep learning important features ('DeepLIFT') described by Shrikumar et al. (2017) <doi:10.48550/arXiv.1704.02685> and gradient-based methods like 'SmoothGrad' described by Smilkov et al. (2017) <doi:10.48550/arXiv.1706.03825>, 'Gradient x Input' or 'Vanilla Gradient'. Details can be found in the accompanying scientific paper: Koenen & Wright (2024, Journal of Statistical Software, <doi:10.18637/jss.v111.i08>).

Package details

AuthorNiklas Koenen [aut, cre] (<https://orcid.org/0000-0002-4623-8271>), Raphael Baudeu [ctb]
MaintainerNiklas Koenen <niklas.koenen@gmail.com>
LicenseMIT + file LICENSE
Version0.3.2
URL https://bips-hb.github.io/innsight/ https://github.com/bips-hb/innsight/
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("innsight")

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innsight documentation built on April 3, 2025, 10:33 p.m.