r descr_models("boost_tree", "C5.0")
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.
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.
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
.
The "Fitting and Predicting with parsnip" article contains examples for boost_tree()
with the "C5.0"
engine.
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