r descr_models("C5_rules", "C5.0")
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:
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()]).
r uses_extension("C5_rules", "C5.0", "classification")
library(rules) C5_rules( trees = integer(1), min_n = integer(1) ) %>% set_engine("C5.0") %>% set_mode("classification") %>% translate()
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.
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