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 :sweat_smile:

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 -0.016400048  -0.0032800095
#> 2  0.212613378   0.0425226755
#> 3 -0.007915047  -0.0015830093
#> 4 -0.001320884  -0.0002641768
#> 5  0.016452991   0.0032905983