In parsnip: A Common API to Modeling and Analysis Functions

r descr_models("decision_tree", "C5.0")

Tuning Parameters

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

param <-
 decision_tree() %>% 
  set_engine("C5.0") %>% 
  set_mode("classification") %>% 
  make_parameter_list(defaults)

This model has r nrow(param) tuning parameters:

param$item

Translation from parsnip to the original package (classification)

decision_tree(min_n = integer()) %>% 
  set_engine("C5.0") %>% 
  set_mode("classification") %>% 
  translate()

[C5.0_train()] is a wrapper around [C50::C5.0()] that makes it easier to run this model.

Preprocessing requirements

Case weights

Saving fitted model objects

Examples

The "Fitting and Predicting with parsnip" article contains examples for decision_tree() with the "C5.0" engine.

References

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

parsnip documentation built on Aug. 18, 2023, 1:07 a.m.