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.

### 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.

### 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 ranger package used in the rforest tool is ranger.