In tidymodels/parsnip: A Common API to Modeling and Analysis Functions

r descr_models("bart", "dbarts")

Tuning Parameters

defaults <- 
  tibble::tibble(parsnip = c("trees", "prior_terminal_node_coef",
                             "prior_terminal_node_expo", "prior_outcome_range"),
                 default = c("200L", "0.95", "2.00", "2.00")
  )

param <-
 bart() %>% 
  set_engine("dbarts") %>% 
  set_mode("regression") %>% 
  make_parameter_list(defaults)

This model has r nrow(param) tuning parameters:

param$item

Parsnip changes the default range for trees to c(50, 500).

Important engine-specific options

Some relevant arguments that can be passed to set_engine():

keepevery, n.thin: Every keepevery draw is kept to be returned to the user. Useful for "thinning" samples.
ntree, n.trees: The number of trees in the sum-of-trees formulation.
ndpost, n.samples: The number of posterior draws after burn in, ndpost / keepevery will actually be returned.
nskip, n.burn: Number of MCMC iterations to be treated as burn in.
nchain, n.chains: Integer specifying how many independent tree sets and fits should be calculated.
nthread, n.threads: Integer specifying how many threads to use. Depending on the CPU architecture, using more than the number of chains can degrade performance for small/medium data sets. As such some calculations may be executed single threaded regardless.
combinechains, combineChains: Logical; if TRUE, samples will be returned in arrays of dimensions equal to nchain times ndpost times number of observations.

Translation from parsnip to the original package (classification)

bart(
  trees = integer(1),
  prior_terminal_node_coef = double(1),
  prior_terminal_node_expo = double(1),
  prior_outcome_range = double(1)
) %>% 
  set_engine("dbarts") %>% 
  set_mode("classification") %>% 
  translate()

Translation from parsnip to the original package (regression)

bart(
  trees = integer(1),
  prior_terminal_node_coef = double(1),
  prior_terminal_node_expo = double(1),
  prior_outcome_range = double(1)
) %>% 
  set_engine("dbarts") %>% 
  set_mode("regression") %>% 
  translate()