*2.6. Selecting Variables Using the VSURF Package*

The VSURF package is a powerful tool for variable selection in regression problems using the RF algorithm. It is a three-step process that involves eliminating irrelevant variables, selecting relevant variables for interpretation, and improving prediction accuracy by removing redundant variables. To begin, the first step of the process involves identifying and eliminating irrelevant variables from the dataset. In the second step, all variables that are associated with the response variable are selected for interpretation. Finally, in the third step, redundant variables are removed to enhance the model's prediction performance. Once the relevant variables have been selected, the minimum mean square error (MSE) is used to determine the optimal number of decision trees (ntree) and the number of variables (mtry) to be used in the RF model. Initially, the ntree parameter is set to 500 and mtry parameter is set to the total number of variables. Once the optimal parameters are calculated, the RF regression model is established and tested.
