I have been currently searching for a method to obtain the best fit α (significance level) for a Conditional Inference Tree model (using the party package) in RStudio. I have just realized I've been using the default value (α=0.05) for my model.
I've been searching everywhere for some code I could apply to get said α, but I can only find it for division trees, not for conditional inference (using ctree). Could someone help me and explain or write down an example I could apply to my model? Thank you very much in advance! Every bit of help is much appreciated!
This can be tuned in the "usual" way, e.g., using cross-validation etc. There are a number of packages that facilitate such tuning tasks. One implementation that is particularly convenient for tuning the significance level in
ctree()iscaret. See the "Conditional Inference Tree" sections in: https://topepo.github.io/caret/train-models-by-tag.html#Accepts_Case_Weights.Other packages that provide convenient general interfaces to
ctree()include mlr3 or tidymodels.