In parsnip: A Common API to Modeling and Analysis Functions

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r descr_models("C5_rules", "C5.0")

Tuning Parameters

#| label: C5.0-param-info
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defaults <- 
  tibble::tibble(parsnip = c("trees", "min_n"),
                 default = c("1L",       "2L"))

param <-
  C5_rules() |> 
  set_engine("C5.0") |> 
  make_parameter_list(defaults)

This model has r nrow(param) tuning parameters:

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param$item

Note that C5.0 has a tool for early stopping during boosting where less iterations of boosting are performed than the number requested. C5_rules() turns this feature off (although it can be re-enabled using [C50::C5.0Control()]).

Translation from parsnip to the underlying model call (classification)

r uses_extension("C5_rules", "C5.0", "classification")

#| label: C5.0-cls
library(rules)

C5_rules(
  trees = integer(1),
  min_n = integer(1)
) |>
  set_engine("C5.0") |>
  set_mode("classification") |>
  translate()

Preprocessing requirements

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

Case weights

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

Saving fitted model objects

#| child: template-butcher.Rmd

References

Quinlan R (1992). "Learning with Continuous Classes." Proceedings of the 5th Australian Joint Conference On Artificial Intelligence, pp. 343-348.
Quinlan R (1993)."Combining Instance-Based and Model-Based Learning." Proceedings of the Tenth International Conference on Machine Learning, pp. 236-243.
Kuhn M and Johnson K (2013). Applied Predictive Modeling. Springer.