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
CMD-enter on mac) to generate results.
The model can be “tuned” by changing the
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