Estimate a Random Forest
To create a Random Forest, first select the type (i.e.,
Classification or Regression), response variable, and one or more
explanatory variables. Press the Estimate model button or
CTRL-enter (CMD-enter on mac) to generate
results.
The model can be “tuned” by changing the mtry,
# trees, Min node size, and
Sample fraction inputs. The best way to determine the
optimal values for these hyper parameters is to use Cross-Validation. In
radiant, you can use the cv.rforest 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.
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 ranger package used in the
rforest tool is ranger.