mlr_learners_regr.bart: Regression BART (Bayesian Additive Regression Trees) Learner

In mlr-org/mlr3extralearners: Extra Learners For mlr3

Regression BART (Bayesian Additive Regression Trees) Learner

Description

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

• Task type: “regr”

• 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	NULL		-
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	NULL		-
splitprobs	untyped	NULL		-
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.

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrBart

Methods

Public methods

• LearnerRegrBart$new()

• LearnerRegrBart$clone()

Inherited methods

• mlr3::Learner$base_learner()
• mlr3::Learner$format()
• mlr3::Learner$help()
• mlr3::Learner$predict()
• mlr3::Learner$predict_newdata()
• mlr3::Learner$print()
• mlr3::Learner$reset()
• mlr3::Learner$train()

---

Method new()

Creates a new instance of this R6 class.

Usage

LearnerRegrBart$new()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerRegrBart$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Author(s)

ck37

References

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.

See Also

• Dictionary of Learners: mlr3::mlr_learners.

• as.data.table(mlr_learners) for a table of available Learners in the running session (depending on the loaded packages).

• Chapter in the mlr3book: https://mlr3book.mlr-org.com/basics.html#learners

• mlr3learners for a selection of recommended learners.

• mlr3cluster for unsupervised clustering learners.

• mlr3pipelines to combine learners with pre- and postprocessing steps.

• mlr3tuning for tuning of hyperparameters, mlr3tuningspaces for established default tuning spaces.

Examples

learner = mlr3::lrn("regr.bart")
print(learner)

# available parameters:
learner$param_set$ids()

mlr-org/mlr3extralearners documentation built on April 13, 2024, 5:25 a.m.