In parsnip: A Common API to Modeling and Analysis Functions

r descr_models("decision_tree", "spark")

Tuning Parameters

defaults <- 
  tibble::tibble(parsnip = c("tree_depth", "min_n"),
                 default = c("5L", "1L"))

param <-
 decision_tree() %>% 
  set_engine("spark") %>% 
  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)) %>% 
  set_engine("spark") %>% 
  set_mode("classification") %>% 
  translate()

Translation from parsnip to the original package (regression)

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

Preprocessing requirements

Case weights

Note that, for spark engines, the case_weight argument value should be a character string to specify the column with the numeric case weights.

Other details

References

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

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