r descr_models("decision_tree", "rpart")
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
decision_tree(tree_depth = integer(1), min_n = integer(1), cost_complexity = double(1)) %>% set_engine("rpart") %>% set_mode("classification") %>% translate()
decision_tree(tree_depth = integer(1), min_n = integer(1), cost_complexity = double(1)) %>% set_engine("rpart") %>% set_mode("regression") %>% translate()
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()
The "Fitting and Predicting with parsnip" article contains examples for decision_tree()
with the "rpart"
engine.
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