In parsnip: A Common API to Modeling and Analysis Functions

r descr_models("cubist_rules", "Cubist")

Tuning Parameters

defaults <- 
  tibble::tibble(parsnip = c("committees", "neighbors",  "max_rules"),
                 default = c("1L",                "0L", "NA_integer"))

param <-
  cubist_rules() %>% 
  set_engine("Cubist") %>% 
  make_parameter_list(defaults)

This model has r nrow(param) tuning parameters:

param$item

Translation from parsnip to the underlying model call (regression)

r uses_extension("cubist_rules", "Cubist", "regression")

library(rules)

cubist_rules(
  committees = integer(1),
  neighbors = integer(1),
  max_rules = integer(1)
) %>%
  set_engine("Cubist") %>%
  set_mode("regression") %>%
  translate()

Preprocessing requirements

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.