Problem Description

I am currently working with simple decision tree models to generate rules to classify respondents of a survey.

The output of each model is "translated" to the complete rule-set of each end node. I then select nodes based on some statistical criteria to generate a new segment.

Now I am struggling to find an easy way to summarize the "rules" needed to classify this segment.

Current Approach

At the moment I extract the rules for each node and then give out the rules for the bigger segment by simply chaining the rules for each node:


extract_rules <- function(model) {
x = rpart.rules(
    style = "wide",
    cover = T,
    nn = T,
    eq = "=",
    facsep = ";"
  rules = rep("", nrow(x))
  for (r in 1:nrow(x)) {
    for (c in 4:(length(x) - 1)) {
      rules[r] = paste0(rules[r], x[r, c])
    rules[r] = gsub("&", " & ", rules[r], fixed = T)

treemodel$frame %>%
  mutate(nn = row.names(.)) %>%
  filter(var == "<leaf>") %>%
    "Node ID" = nn,
    Casecount = n,
    PopReach = wt,
    TGHitrate = yval
  ) %>%
  arrange(desc(TGHitrate)) %>%
  cbind(rules = extract_rules(treemodel))

However this generates unnecessarily complex rules because if a segment consists of all end nodes of a specific leaf the easiest rule would of course be the leaf rule not an "OR-chain" of all end nodes. E.g.

male == yes

is much easier to understand then

male == yes & age < 25 OR male == yes & age >= 25

What would code look like that identifies the overarching rule set needed to classify a bigger segment consisting of several nodes?

  • $\begingroup$ Are you looking to optimize the above example like a compiler? Find the redundant age condition, remove, find the male == yes on both sides of the OR and reduce? However I am guessing the tree split with age at 25 to put a different decision in the leaf nodes and optimizing those into 1 rule would eliminate the different decision. You could look at RuleFit here and here which may do much of what you want. $\endgroup$
    – Craig
    Feb 14, 2022 at 10:30
  • $\begingroup$ @Craig yes a compiler would solve the practical issue I have, as the rules are too complex and in practice would be summed up manually exactly like you describe (i.e. just use male == yes and drop the age). From a theoretical point of view, what I would much rather do is parse the tree model vertically and output the rules of the relevant leafs and nodes based on end node selection. I am looking into the links you posted, thank you. $\endgroup$
    – Fnguyen
    Feb 14, 2022 at 10:37


Your Answer

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge you have read our privacy policy.

Browse other questions tagged or ask your own question.