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)
Important engine-specific options
Some relevant arguments that can be passed to set_engine()
:
keepevery
,n.thin
: Everykeepevery
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; ifTRUE
, samples will be returned in arrays of dimensions equal tonchain
timesndpost
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()
## 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)
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()
## 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.