Bayesian Additive Regression Trees are similar to gradient boosting algorithms. Calls dbarts::bart() from dbarts.

## Dictionary

This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn():

mlr_learners\$get("regr.bart")
lrn("regr.bart")

## Meta Information

• Predict Types: “response”

• Feature Types: “integer”, “numeric”, “factor”, “ordered”

• Required Packages: mlr3, mlr3extralearners, dbarts

## Parameters

 Id Type Default Levels Range ntree integer 200 $$[1, \infty)$$ sigest untyped - sigdf integer 3 $$[1, \infty)$$ sigquant numeric 0.9 $$[0, 1]$$ k numeric 2 $$[0, \infty)$$ power numeric 2 $$[0, \infty)$$ base numeric 0.95 $$[0, 1]$$ ndpost integer 1000 $$[1, \infty)$$ nskip integer 100 $$[0, \infty)$$ printevery integer 100 $$[0, \infty)$$ keepevery integer 1 $$[1, \infty)$$ keeptrainfits logical TRUE TRUE, FALSE - usequants logical FALSE TRUE, FALSE - numcut integer 100 $$[1, \infty)$$ printcutoffs integer 0 $$(-\infty, \infty)$$ verbose logical FALSE TRUE, FALSE - nthread integer 1 $$(-\infty, \infty)$$ keeptrees logical FALSE TRUE, FALSE - keepcall logical TRUE TRUE, FALSE - sampleronly logical FALSE TRUE, FALSE - seed integer NA $$(-\infty, \infty)$$ proposalprobs untyped - splitprobs untyped - keepsampler logical - TRUE, FALSE -

## Custom mlr3 parameters

• Parameter: offset

• The parameter is removed, because only dbarts::bart2 allows an offset during training, and therefore the offset parameter in dbarts:::predict.bart is irrelevant for dbarts::dbart.

• Parameter: nchain, combineChains, combinechains

• The parameters are removed as parallelization of multiple models is handled by future.

## Initial parameter values

• keeptrees is initialized to TRUE because it is required for prediction.

Sparapani, Rodney, Spanbauer, Charles, McCulloch, Robert (2021). “Nonparametric machine learning and efficient computation with bayesian additive regression trees: the BART R package.” Journal of Statistical Software, 97, 1--66.

Chipman, A H, George, I E, McCulloch, E R (2010). “BART: Bayesian additive regression trees.” The Annals of Applied Statistics, 4(1), 266--298.