Decision Trees and Random Forests for Grouped Variables

Attempt to create an R package from prototype available at “Classification tree algorithm for grouped variables”.

Installation

Don’t try to install this package 😅

Example

This is a basic example which shows you how to solve a common problem:

library(dtrfgv)

data(rfgv_dataset)
data(group)

data  <- rfgv_dataset 
train <- data[which(data[,1]=="train"),-1]           # negative index into the `data` 
test  <- data[which(data[,1]=="test"),-1]             # object specifying all rows and all columns 
validation<-data[which(data[,1]=="validation"),-1] # except the first column.

forest<-rfgv(train,
             group=group,
             groupImp=group,
             ntree=4,
             mtry_group=3,
             sampvar=TRUE,
             maxdepth=2,
             replace=TRUE,
             case_min=1,
             sampsize=nrow(train),
             mtry_var=rep(2,5),
             grp.importance=TRUE,
             test=test,
             keep_forest=FALSE,
             crit=1,
             penalty="No")
  
print(forest$importance)
#>   MeanDecrAcc MeanDecrAccNor
#> 1  0.01260190    0.002520380
#> 2  0.19578711    0.039157422
#> 3 -0.01797763   -0.003595526
#> 4 -0.01288626   -0.002577253
#> 5 -0.01007369   -0.002014739