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>).
| Version: | 0.3.2 |
| Depends: | R (≥ 3.5.0) |
| Imports: | checkmate,cli,ggplot2, methods,R6,torch |
| Suggests: | covr,fastshap,GGally, grid,gridExtra,gtable,keras,knitr,lime,luz,neuralnet,palmerpenguins,plotly,rmarkdown,ranger,spelling,tensorflow,testthat (≥ 3.0.0) |
| Published: | 2025-03-30 |
| DOI: | 10.32614/CRAN.package.innsight |
| Author: | Niklas Koenen [aut, cre], Raphael Baudeu [ctb] |
| Maintainer: | Niklas Koenen <niklas.koenen at gmail.com> |
| BugReports: | https://github.com/bips-hb/innsight/issues/ |
| License: | MIT + fileLICENSE |
| URL: | https://bips-hb.github.io/innsight/,https://github.com/bips-hb/innsight/ |
| NeedsCompilation: | no |
| Language: | en-US |
| Citation: | innsight citation info |
| Materials: | README,NEWS |
| CRAN checks: | innsight results |
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