Find the maximum value in decision tree

Question

I created decision tree with Party package in R. I'm trying to get the route/branch with the maximum value.

It can be mean value that came from box-plot

and it can be probability value that came from binary tree
_{(source: rdatamining.com)}

Answer 1

This can be done pretty easily actually, though while your definition of maximum value is clear for a regression tree, it is not very clear for a classification tree, as in each node different level can have it's own maximum

Either way, here's a pretty simple helper function that will return you the predictions for each type of tree

GetPredicts <- function(ct){
      f <- function(ct, i) nodes(ct, i)[[1]]$prediction
      Terminals <- unique(where(ct))
      Predictions <- sapply(Terminals, f, ct = ct)
      if(is.matrix(Predictions)){
        colnames(Predictions) <- Terminals
        return(Predictions)
       } else {
        return(setNames(Predictions, Terminals))
       }
}

Now luckily you've took your trees from the examples of ?ctree, so we can test them (next time, please provide the code you used yourself)

---

Regression Tree (your frist tree)

## load the package and create the tree
library(party)
airq <- subset(airquality, !is.na(Ozone))
airct <- ctree(Ozone ~ ., data = airq, 
               controls = ctree_control(maxsurrogate = 3))
plot(airct)

Now, test the function

res <- GetPredicts(airct)
res
#        5        3        6        9        8 
# 18.47917 55.60000 31.14286 48.71429 81.63333 

So we've got the predictions per each terminal node. You can easily proceed with which.max(res) from here (I'll leave it for you to decide)

---

Classification tree (your second tree)

irisct <- ctree(Species ~ .,data = iris)
plot(irisct, type = "simple")

Run the function

res <- GetPredicts(irisct)
res
#      2          5   6          7
# [1,] 1 0.00000000 0.0 0.00000000
# [2,] 0 0.97826087 0.5 0.02173913
# [3,] 0 0.02173913 0.5 0.97826087

Now, the output is a bit harder to read because each class has it's own probabilities. You could make this a bit more readable using

row.names(res) <- levels(iris$Species)
res
#            2          5   6          7
# setosa     1 0.00000000 0.0 0.00000000
# versicolor 0 0.97826087 0.5 0.02173913
# virginica  0 0.02173913 0.5 0.97826087

The, you could do something like the following in order to get the overall maximum value

which(res == max(res), arr.ind = TRUE)
#        row col
# setosa   1   1

For column/row maxes, you could do

matrixStats::colMaxs(res)
# [1] 1.0000000 0.9782609 0.5000000 0.9782609
matrixStats::rowMaxs(res)
# [1] 1.0000000 0.9782609 0.9782609

But, again, I'll leave to you to decide on how to proceed from here.