Analysis functions to quantify inputs importance in neural network models.
Functions are available for calculating and plotting the inputs importance and obtaining
the activation function of each neuron layer and its derivatives. The importance of a given
input is defined as the distribution of the derivatives of the output with respect to that
input in each training data point <doi:10.18637/jss.v102.i07>.
| Version: |
1.0.1 |
| Imports: |
ggplot2, gridExtra, NeuralNetTools, reshape2, caret, fastDummies, stringr, Hmisc, ggforce, scales, ggnewscale, magrittr |
| Suggests: |
h2o, RSNNS, nnet, neuralnet, plotly, e1071 |
| Published: |
2022-06-21 |
| Author: |
José Portela González [aut],
Antonio Muñoz San Roque [aut],
Jaime Pizarroso Gonzalo [aut, ctb, cre] |
| Maintainer: |
Jaime Pizarroso Gonzalo <jpizarroso at comillas.edu> |
| BugReports: |
https://github.com/JaiPizGon/NeuralSens/issues |
| License: |
GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: |
https://github.com/JaiPizGon/NeuralSens |
| NeedsCompilation: |
no |
| Citation: |
NeuralSens citation info |
| CRAN checks: |
NeuralSens results |