rpart Complexity Parameter values - r

Question

rpart parameters can be found using getModelInfo
getModelInfo("rpart")[[1]]$grid
function(x, y, len = NULL, search = "grid"){
dat <- if(is.data.frame(x)) x else as.data.frame(x)
dat$.outcome <- y
initialFit <- rpart(.outcome ~ .,
data = dat,
control = rpart.control(cp = 0))$cptable
initialFit <- initialFit[order(-initialFit[,"CP"]), , drop = FALSE]
if(search == "grid") {
if(nrow(initialFit) < len) {
tuneSeq <- data.frame(cp = seq(min(initialFit[, "CP"]),
max(initialFit[, "CP"]),
length = len))
} else tuneSeq <- data.frame(cp = initialFit[1:len,"CP"])
colnames(tuneSeq) <- "cp"
} else {
tuneSeq <- data.frame(cp = unique(sample(initialFit[, "CP"], size = len, replace = TRUE)))
}
tuneSeq
}
the only parameter is
cp = seq(min(initialFit[, "CP"]), max(initialFit[, "CP"]),length = len)
But how can I get the initialFit and the len?
Searching elsewhere I found that cp can usually take 10 values from 0.18 to 0.01. But still couldn't find out where those values come from

Answer 1

If you're unsure about appropriate values for a parameter, you can make caret choose for you and use default values. Here is an example that works end-to-end without explicitly specifying cp:
library(tidyverse)
library(caret)
library(forcats)
# Take mtcars data for example
df <- mtcars %>%
# Which cars are automatic, which ones are manual?
mutate(am = as.factor(am),
am = fct_recode(am, 'automatic' = '1', 'manual' = '0'))
set.seed(123456)
fitControl <- trainControl(method = 'repeatedcv',
number = 10,
repeats = 10,
classProbs = TRUE,
summaryFunction = twoClassSummary)
# Run rpart
# Tuning grid is left unspecified, so caret uses the default
tree1 <- train(am ~ .,
df,
method = 'rpart',
tuneLength = 20,
metric = 'ROC',
trControl = fitControl)
Alternatively, if you want to explicitly specify cp, do so using the tuning grid:
tuneGrid <- expand.grid(cp = seq(0, 0.05, 0.005))
tree2 <- train(am ~ .,
df,
method = 'rpart',
tuneLength = 20,
metric = 'ROC',
trControl = fitControl,
tuneGrid = tuneGrid)
A question on why you should select which values for cp is probably better posted on CrossValidated.
Update:
To answer your follow-on question about the default values and the values I chose in my example, I recommend going back to the primary source of the modelling function. caret is a great package for convenience reasons, but all it does is making lots of algorithms more accessible through a shared syntax. If you have a technical question about rpart, consult the package manual here.
As mentioned above, this type of question is better placed on CrossValidated, where the focus is on maths, stats, and machine learning.
However, to give you a tldr here:
The choice of tuning grid parameters is always going to be arbitrary to some extent. The objective is to find the value that produces the best results for your specific problem, which in turn depends on your data, your algorithm, and your evaluation metric. Some common "rules of thumb" include to start with a wide range, identify the area with a likely maximum and then use finer intervals around that region. In your case it is relatively easy as you only have one parameter to optimise over. Just try a couple of values and see what happens. You can plot the fitted tree object (plot(tree1)) to see how your model improves as a function of the complexity parameter cp. Eventually you will start developing a "feel" and "intuition" for what might work.