In parsnip: A Common API to Modeling and Analysis Functions

#| child: aaa.Rmd
#| include: false

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

Tuning Parameters

#| label: C5.0-param-info
#| echo: false
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:

#| label: C5.0-param-list
#| echo: false
#| results: asis
param$item

Translation from parsnip to the original package (classification)

#| label: C5.0-cls
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

#| child: template-tree-split-factors.Rmd

Case weights

#| child: template-uses-case-weights.Rmd

Saving fitted model objects

#| child: template-butcher.Rmd

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 June 8, 2025, 12:10 p.m.