Classification BART (Bayesian Additive Regression Trees) Learner

Bayesian Additive Regression Trees are similar to gradient boosting algorithms. The classification problem is solved by 0-1 encoding of the two-class targets and setting the decision threshold to p = 0.5 during the prediction phase. 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("classif.bart")
lrn("classif.bart")

Meta Information

• Task type: “classif”

• Predict Types: “response”, “prob”

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

• Required Packages: mlr3, mlr3extralearners, dbarts

Parameters

Id	Type	Default	Levels	Range
ntree	integer	200		\([1, \infty)\)
k	numeric	2		\([0, \infty)\)
power	numeric	2		\([0, \infty)\)
base	numeric	0.95		\([0, 1]\)
binaryOffset	numeric	0		\((-\infty, \infty)\)
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	TRUE	TRUE, FALSE	-
keepcall	logical	TRUE	TRUE, FALSE	-
sampleronly	logical	FALSE	TRUE, FALSE	-
seed	integer	NA		\((-\infty, \infty)\)
proposalprobs	untyped			-
splitprobs	untyped			-

Parameter Changes

• Parameter: keeptrees

• Original: FALSE

• New: TRUE

• Reason: Required for prediction

• 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: nthread, nchain, combineChains, combinechains

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

• Parameter: sigest, sigdf, sigquant, keeptres

• Regression only.

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.

Author

ck37

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifBart

Methods

Public methods

• LearnerClassifBart$new()

• LearnerClassifBart$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

LearnerClassifBart$new()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClassifBart$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

learner = mlr3::lrn("classif.bart")
print(learner)
#> <LearnerClassifBart:classif.bart>: Bayesian Additive Regression Trees
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, mlr3extralearners, dbarts
#> * Predict Type: response
#> * Feature types: integer, numeric, factor, ordered
#> * Properties: twoclass, weights

# available parameters:
learner$param_set$ids()
#>  [1] "ntree"         "k"             "power"         "base"         
#>  [5] "binaryOffset"  "ndpost"        "nskip"         "printevery"   
#>  [9] "keepevery"     "keeptrainfits" "usequants"     "numcut"       
#> [13] "printcutoffs"  "verbose"       "keepcall"      "sampleronly"  
#> [17] "seed"          "proposalprobs" "splitprobs"