问题
I used the CHAID package from this link ..It gives me a chaid object which can be plotted..I want a decision table with each decision rule in a column instead of a decision tree. .But i dont understand how to access nodes and paths in this chaid object..Kindly help me.. I followed the procedure given in this link
i cant post my data here since it is too long.So i am posting a code which takes the sample dataset provided with chaid to perform the task.
copied from help manual of chaid:
library("CHAID")
### fit tree to subsample
set.seed(290875)
USvoteS <- USvote[sample(1:nrow(USvote), 1000),]
ctrl <- chaid_control(minsplit = 200, minprob = 0.1)
chaidUS <- chaid(vote3 ~ ., data = USvoteS, control = ctrl)
print(chaidUS)
plot(chaidUS)
Output:
Model formula:
vote3 ~ gender + ager + empstat + educr + marstat
Fitted party:
[1] root
| [2] marstat in married
| | [3] educr <HS, HS, >HS: Gore (n = 311, err = 49.5%)
| | [4] educr in College, Post Coll: Bush (n = 249, err = 35.3%)
| [5] marstat in widowed, divorced, never married
| | [6] gender in male: Gore (n = 159, err = 47.8%)
| | [7] gender in female
| | | [8] ager in 18-24, 25-34, 35-44, 45-54: Gore (n = 127, err = 22.0%)
| | | [9] ager in 55-64, 65+: Gore (n = 115, err = 40.9%)
Number of inner nodes: 4
Number of terminal nodes: 5
So my question is how to get this tree data in a decision table with each decision rule(branch/path) in a column..I dont understand how to access different tree paths from this chaid object..
回答1:
CHAID package uses partykit (recursive partitioning) tree structures. You can walk the tree by using party nodes - a node can be terminal or have a list of nodes with information about decision rule (split) and fitted data.
The code below walks the tree and creates the decision table. It is written for demonstration purposes and tested only on one sample tree.
tree2table <- function(party_tree) {
df_list <- list()
var_names <- attr( party_tree$terms, "term.labels")
var_levels <- lapply( party_tree$data, levels)
walk_the_tree <- function(node, rule_branch = NULL) {
# depth-first walk on partynode structure (recursive function)
# decision rules are extracted for every branch
if(missing(rule_branch)) {
rule_branch <- setNames(data.frame(t(replicate(length(var_names), NA))), var_names)
rule_branch <- cbind(rule_branch, nodeId = NA)
rule_branch <- cbind(rule_branch, predict = NA)
}
if(is.terminal(node)) {
rule_branch[["nodeId"]] <- node$id
rule_branch[["predict"]] <- predict_party(party_tree, node$id)
df_list[[as.character(node$id)]] <<- rule_branch
} else {
for(i in 1:length(node)) {
rule_branch1 <- rule_branch
val1 <- decision_rule(node,i)
rule_branch1[[names(val1)[1]]] <- val1
walk_the_tree(node[i], rule_branch1)
}
}
}
decision_rule <- function(node, i) {
# returns split decision rule in data.frame with variable name an values
var_name <- var_names[node$split$varid[[1]]]
values_vec <- var_levels[[var_name]][ node$split$index == i]
values_txt <- paste(values_vec, collapse = ", ")
return( setNames(values_txt, var_name))
}
# compile data frame list
walk_the_tree(party_tree$node)
# merge all dataframes
res_table <- Reduce(rbind, df_list)
return(res_table)
}
call function with the CHAID tree object:
table1 <- tree2table(chaidUS)
the result should be something like this:
gender ager empstat educr marstat nodeId predict
-------- -------------------------- --------- ------------------ -------------------------------- -------- ---------
NA NA NA <HS, HS, >HS married 3 Gore
NA NA NA College, Post Coll married 4 Bush
male NA NA NA widowed, divorced, never married 6 Gore
female 18-24, 25-34, 35-44, 45-54 NA NA widowed, divorced, never married 8 Gore
female 55-64, 65+ NA NA widowed, divorced, never married 9 Gore
来源：https://stackoverflow.com/questions/28428508/chaid-regression-tree-to-table-conversion-in-r