In parsnip: A Common API to Modeling and Analysis Functions

r descr_models("decision_tree", "rpart")

Tuning Parameters

defaults <- 
  tibble::tibble(parsnip = c("tree_depth", "min_n", "cost_complexity"),
                 default = c("30L", "2L", "0.01"))

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

This model has r nrow(param) tuning parameters:

param$item

Translation from parsnip to the original package (classification)

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

Translation from parsnip to the original package (regression)

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

Translation from parsnip to the original package (censored regression)

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

library(censored)

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

Preprocessing requirements

Case weights

Other details

Saving fitted model objects

Examples

The "Fitting and Predicting with parsnip" article contains examples for decision_tree() with the "rpart" engine.

References

• Kuhn, M, and K Johnson. 2013. Applied Predictive Modeling. Springer.

parsnip documentation built on June 24, 2024, 5:14 p.m.