> 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. ### Report > Rmd 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. ### R-functions 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`.