Contents
Why are explanatory variables important in a neural network?
A general goal of statistical modelling is to identify the relative importance of explanatory variables for their relation to one or more response variables. The plotting function is used to portray the neural network in this manner, or more specifically, it plots the neural network as a neural interpretation diagram (NID) 1.
How to quantify the importance of neural net inputs?
What you describe is indeed one standard way of quantifying the importance of neural-net inputs. Note that in order for this to work, however, the input variables must be normalized in some way. Otherwise weights corresponding to input variables that tend to have larger values will be proportionally smaller.
How are weights used in a neural network?
The weights that connect variables in a neural network are partially analogous to parameter coefficients in a standard regression model and can be used to describe relationships between variables.
How is the plotting function used in neural networks?
The plotting function is used to portray the neural network in this manner, or more specifically, it plots the neural network as a neural interpretation diagram (NID) 1. The rationale for use of an NID is to provide insight into variable importance by visually examining the weights between the layers.
How does the variable importance of a predictor work?
For most classification models, each predictor will have a separate variable importance for each class (the exceptions are classification trees, bagged trees and boosted trees). All measures of importance are scaled to have a maximum value of 100, unless the scale argument of varImp.train is set to FALSE.
How to train a neural network for multivariate regression?
The problem falls into Multivariate Regression category since the outputs are continuous value. Therefore, you can train a neural network (NN) having 4 output nodes and input feature vector of size 4. A sample NN model having one hidden layer using tensorfow is as follows: