In parsnip: A Common API to Modeling and Analysis Functions

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

Tuning Parameters

#| label: C5.0-param-info
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defaults <- 
  tibble::tibble(parsnip = c("trees", "min_n", "sample_size"),
                 default = c("15L", "2L", "1.0"))

param <-
 boost_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
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param$item

The implementation of C5.0 limits the number of trees to be between 1 and 100.

Translation from parsnip to the original package (classification)

#| label: C5.0-cls
boost_tree(trees = integer(), min_n = integer(), sample_size = numeric()) |> 
  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

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Saving fitted model objects

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Other details

Early stopping

By default, early stopping is used. To use the complete set of boosting iterations, pass earlyStopping = FALSE to [set_engine()]. Also, it is unlikely that early stopping will occur if sample_size = 1.

Examples

The "Fitting and Predicting with parsnip" article contains examples for boost_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.