In topepo/parsnip: A Common API to Modeling and Analysis Functions

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

Tuning Parameters

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:

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)

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

Case weights

Saving fitted model objects

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.

topepo/parsnip documentation built on April 16, 2024, 3:23 a.m.