r descr_models("bag_tree", "rpart")
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.
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()
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()
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()
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.
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