In parsnip: A Common API to Modeling and Analysis Functions

r descr_models("decision_tree", "rpartScore")

Tuning parameters

defaults <-
  tibble::tibble(
    parsnip = c("tree_depth", "min_n", "cost_complexity"),
    default = c("30L", "2L", "0.01")
  )

param <-
  decision_tree() |>
  set_engine("rpartScore") |>
  set_mode("classification") |>
  make_parameter_list(defaults)

This model has r nrow(param) tuning parameters:

param$item

Translation from parsnip to the original package

#| label: rpart-cls-decision-tree-ordered
decision_tree(
  tree_depth = integer(1),
  min_n = integer(1),
  cost_complexity = double(1)
) |>
  set_engine("rpartScore") |>
  set_mode("classification") |>
  translate()

Preprocessing requirements

#| child: template-tree-split-factors.Rmd

Case weights

#| child: template-uses-case-weights.Rmd

Prediction types

#| label: predict-types
parsnip:::get_from_env("decision_tree_predict") |>
  dplyr::filter(engine == "rpartScore") |>
  dplyr::select(mode, type)

References

Galimberti G, Soffritti G, Di Maso M. 2012. Classification Trees for Ordinal Responses in R: The rpartScore Package. Journal of Statistical Software 47(10):1-25. \doi{10.18637/jss.v047.i10}.
Kuhn, M, and K Johnson. 2013. Applied Predictive Modeling. Springer.