R: Obtaining Rules from a Function

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This isn’t my area of expertise, but perhaps this function (from https://www.togaware.com/datamining/survivor/Convert_Tree.html) will do what you want to do:

library(rpart)
car.test.frame$Reliability = as.factor(car.test.frame$Reliability)
z.auto <- rpart(Reliability ~ ., car.test.frame)
plot(z.auto, margin = 0.25)
text(z.auto, pretty = TRUE, cex = 0.8,
     splits = TRUE, use.n = TRUE, all = FALSE)

list.rules.rpart <- function(model)
{
  if (!inherits(model, "rpart")) stop("Not a legitimate rpart tree")
  #
  # Get some information.
  #
  frm     <- model$frame
  names   <- row.names(frm)
  ylevels <- attr(model, "ylevels")
  ds.size <- model$frame[1,]$n
  #
  # Print each leaf node as a rule.
  #
  for (i in 1:nrow(frm))
  {
    if (frm[i,1] == "<leaf>")
    {
      # The following [,5] is hardwired - needs work!
      cat("\n")
      cat(sprintf(" Rule number: %s ", names[i]))
      cat(sprintf("[yval=%s cover=%d (%.0f%%) prob=%0.2f]\n",
                  ylevels[frm[i,]$yval], frm[i,]$n,
                  round(100*frm[i,]$n/ds.size), frm[i,]$yval2[,5]))
      pth <- path.rpart(model, nodes=as.numeric(names[i]), print.it=FALSE)
      cat(sprintf("   %s\n", unlist(pth)[-1]), sep="")
    }
  }
}

list.rules.rpart(z.auto)
>Rule number: 4 [yval=3 cover=10 (20%) prob=0.00]
>   Country=Germany,Korea,Mexico,Sweden,USA
>   Weight>=3168
>
> Rule number: 5 [yval=2 cover=18 (37%) prob=4.00]
>   Country=Germany,Korea,Mexico,Sweden,USA
>   Weight< 3168
>
> Rule number: 3 [yval=5 cover=21 (43%) prob=2.00]
>   Country=Japan,Japan/USA

example.png

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