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: 1.1.3 Imports: ggplot2, gridExtra, NeuralNetTools, reshape2, caret, fastDummies, stringr, Hmisc, ggforce, scales, ggnewscale, magrittr, ggrepel, ggbreak, dplyr Suggests: h2o, RSNNS, nnet, neuralnet, plotly, e1071 Published: 2024-05-11 DOI: 10.32614/CRAN.package.NeuralSens 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 resultsRetroSearch is an open source project built by @garambo | Open a GitHub Issue
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