vignettes/pkgdown/nn.Rmd
nn.Rmd
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
.