In parsnip: A Common API to Modeling and Analysis Functions

r descr_models("rand_forest", "ordinalForest")

Tuning Parameters

#| label: ordinalForest-param-info
#| echo: false
defaults <- 
  tibble::tibble(parsnip = c("mtry", "trees", "min_n"),
                 default = c("see below", "500L", "see below"))

param <-
  rand_forest() |> 
  set_engine("ordinalForest") |> 
  make_parameter_list(defaults)

This model has r nrow(param) tuning parameters:

param$item

mtry depends on the number of columns and the model mode. The default in [ordinalForest::ordfor()] is floor(sqrt(ncol(x))).

min_n depends on the mode. For regression, a value of 5 is the default. For classification, a value of 10 is used.

Translation from parsnip to the original package (classification)

rand_forest(
  mtry = integer(1),
  trees = integer(1),
  min_n = integer(1)
) |> 
  set_engine("ordinalForest") |> 
  set_mode("classification") |> 
  translate()

Preprocessing requirements

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

Case weights

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

Other notes

By default, parallel processing is turned off. When tuning, it is more efficient to parallelize over the resamples and tuning parameters. To parallelize the construction of the trees within the model, change the num.threads argument via [set_engine()].

References

Hornung R. 2020. Ordinal Forests. Journal of Classification 37:4-17. \doi{10.1007/s00357-018-9302-x}.
Kuhn, M, and K Johnson. 2013. Applied Predictive Modeling. Springer.