NeuralSens: Sensitivity Analysis of Neural Networks

Understand

Neural networks are important tools for data-intensive analysis and are commonly applied to model non-linear relationships between dependent and independent variables.

• However, neural networks are usually seen as "black boxes" that offer minimal information about how the input variables are used to predict the response in a fitted model.
• This article describes the \pkg{NeuralSens} package that can be used to perform sensitivity analysis of neural networks using the partial derivatives method.
• Functions in the package can be used to obtain the sensitivities of the output with respect to the input variables, evaluate variable importance based on sensitivity measures and characterize relationships between input and output variables.