Estimate a Neural Network
To estimate a model select the type (i.e., Classification or
Regression), response variable, and one or more explanatory variables.
Estimate button or
CMD-enter on mac) to generate results. The model can be
“tuned” by changing the
Size (i.e., the number of nodes in
the hidden layer) and by adjusting the
Decay rate. The
higher the value set for
Decay, the higher the penalty on
the size of (the sum of squares of) the weights. When
is set to 0, the model has the most flexibility to fit the (training)
data accurately. However, without
Decay the model is also
more likely to overfit.
The best way to determine the optimal values for
Decays is to use Cross-Validation. In radiant, you can
cv.nn function for this purpose. See the
for more information.
Add code to
> Rmd to (re)create the analysis by clicking the
icon on the bottom
left of your screen or by pressing
ALT-enter on your
If either a
Garson plot was
created it can be customized using
ggplot2 commands (e.g.,
plot(result, plots = "garson", custom = TRUE) + labs(title = "Garson plot")).
> Visualize for details.
To add, for example, a title to a network plot use
title(main = "Network plot"). See the
graphics documentation for additional information.
For an overview of related R-functions used by Radiant to estimate a neural network model see Model > Neural network.
The key function from the
nnet package used in the
nn tool is