In parsnip: A Common API to Modeling and Analysis Functions

r descr_models("bag_tree", "rpart")

Tuning Parameters

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

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

This model has r nrow(param) tuning parameters:

param$item

For the class_cost parameter, the value can be a non-negative scalar for a class cost (where a cost of 1 means no extra cost). This is useful for when the first level of the outcome factor is the minority class. If this is not the case, values between zero and one can be used to bias to the second level of the factor.

Translation from parsnip to the original package (classification)

r uses_extension("bag_tree", "rpart", "classification")

library(baguette)

bag_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)

r uses_extension("bag_tree", "rpart", "regression")

library(baguette)

bag_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("bag_tree", "rpart", "censored regression")

library(censored)

bag_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

References

Breiman L. 1996. "Bagging predictors". Machine Learning. 24 (2): 123-140
Hothorn T, Lausen B, Benner A, Radespiel-Troeger M. 2004. Bagging Survival Trees. Statistics in Medicine, 23(1), 77–91.
Kuhn, M, and K Johnson. 2013. Applied Predictive Modeling. Springer.