NeuralSens: Sensitivity Analysis of Neural Networks

NeuralSens: Sensitivity Analysis of Neural Networks

neural networks, sensitivity, analysis, variable importance, R, NeuralSens

This article presents the NeuralSens package that can be used to perform sensitivity
analysis of neural networks using the partial derivatives method. The main function of the
package calculates the partial derivatives of the output with regard to the input variables
of a multi-layer perceptron model, which can be used to evaluate variable importance
based on sensitivity measures and characterize relationships between input and output
variables. Methods to calculate partial derivatives are provided for objects trained using
common neural network packages in R, and a ‘numeric’ method is provided for objects
from packages which are not included. The package also includes functions to plot the
information obtained from the sensitivity analysis.
The article contains an overview of techniques for obtaining information from neural
network models, a theoretical foundation of how partial derivatives are calculated, a description of the package functions, and applied examples to compare NeuralSens functions
with analogous functions from other available R packages

Jaime Pizarroso

https://www.jstatsoft.org/article/view/v102i07

Universidad Pontificia
Comillas

April 2022

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English

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