Estimate a Neural Network
To estimate a model select the type (i.e., Classification or
Regression), response variable, and one or more explanatory variables.
Press the Estimate button or CTRL-enter
(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 Decay
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 Size
and Decays is to use Cross-Validation. In radiant, you can
use the cv.nn function for this purpose. See the
documentation
for more information.
Add code to
Report
> Rmd to (re)create the analysis by clicking the
icon on the bottom
left of your screen or by pressing ALT-enter on your
keyboard.
If either a Olden or Garson plot was
created it can be customized using ggplot2 commands (e.g.,
plot(result, plots = "garson", custom = TRUE) + labs(title = "Garson plot")).
See
Data
> Visualize for details.
To add, for example, a title to a network plot use
title(main = "Network plot"). See the
R
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 nnet.