In parsnip: A Common API to Modeling and Analysis Functions

r descr_models("decision_tree", "partykit")

Tuning Parameters

defaults <- 
  tibble::tibble(parsnip = c("tree_depth", "min_n"),
                 default = c("see below", "20L"))

param <-
 decision_tree() %>% 
  set_engine("partykit") %>% 
  set_mode("regression") %>% 
  make_parameter_list(defaults) %>% 
  distinct()

This model has r nrow(param) tuning parameters:

param$item

The tree_depth parameter defaults to 0 which means no restrictions are applied to tree depth.

An engine-specific parameter for this model is:

mtry: the number of predictors, selected at random, that are evaluated for splitting. The default is to use all predictors.

Translation from parsnip to the original package (regression)

r uses_extension("decision_tree", "partykit", "regression")

library(bonsai)

decision_tree(tree_depth = integer(1), min_n = integer(1)) %>% 
  set_engine("partykit") %>% 
  set_mode("regression") %>% 
  translate()

Translation from parsnip to the original package (classification)

r uses_extension("decision_tree", "partykit", "classification")

library(bonsai)

decision_tree(tree_depth = integer(1), min_n = integer(1)) %>% 
  set_engine("partykit") %>% 
  set_mode("classification") %>% 
  translate()

parsnip::ctree_train() is a wrapper around [partykit::ctree()] (and other functions) that makes it easier to run this model.

Translation from parsnip to the original package (censored regression)

r uses_extension("decision_tree", "partykit", "censored regression")

library(censored)

decision_tree(tree_depth = integer(1), min_n = integer(1)) %>% 
  set_engine("partykit") %>% 
  set_mode("censored regression") %>% 
  translate()