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Bayesian additive regression trees via dbarts

Source: R/bart_dbarts.R
details_bart_dbarts.Rd

dbarts::bart() creates an ensemble of tree-based model whose training and assembly is determined using Bayesian analysis.

Details

For this engine, there are multiple modes: classification and regression

Tuning Parameters

This model has 4 tuning parameters:

  • trees: # Trees (type: integer, default: 200L)

  • prior_terminal_node_coef: Terminal Node Prior Coefficient (type: double, default: 0.95)

  • prior_terminal_node_expo: Terminal Node Prior Exponent (type: double, default: 2.00)

  • prior_outcome_range: Prior for Outcome Range (type: double, default: 2.00)

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)

parsnip::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() |>
  print_model_spec()

## BART Model Specification (classification)
##
## Main Arguments:
##   trees = integer(1)
##   prior_terminal_node_coef = double(1)
##   prior_terminal_node_expo = double(1)
##   prior_outcome_range = double(1)
##
## Computational engine: dbarts
##
## Model fit template:
## dbarts::bart(x = missing_arg(), y = missing_arg(), ntree = integer(1),
##     base = double(1), power = double(1), k = double(1), verbose = FALSE,
##     keeptrees = TRUE, keepcall = FALSE)

Translation from parsnip to the original package (regression)

parsnip::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()|>
  print_model_spec()

## BART Model Specification (regression)
##
## Main Arguments:
##   trees = integer(1)
##   prior_terminal_node_coef = double(1)
##   prior_terminal_node_expo = double(1)
##   prior_outcome_range = double(1)
##
## Computational engine: dbarts
##
## Model fit template:
## dbarts::bart(x = missing_arg(), y = missing_arg(), ntree = integer(1),
##     base = double(1), power = double(1), k = double(1), verbose = FALSE,
##     keeptrees = TRUE, keepcall = FALSE)

Preprocessing requirements

Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via fit(), parsnip will convert factor columns to indicators.

dbarts::bart() will also convert the factors to indicators if the user does not create them first.

References

  • Chipman, George, McCulloch. “BART: Bayesian additive regression trees.” Ann. Appl. Stat. 4 (1) 266 - 298, March 2010.