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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

IdTypeDefaultLevelsRange
ntreeinteger200\([1, \infty)\)
sigestuntyped-
sigdfinteger3\([1, \infty)\)
sigquantnumeric0.9\([0, 1]\)
knumeric2\([0, \infty)\)
powernumeric2\([0, \infty)\)
basenumeric0.95\([0, 1]\)
ndpostinteger1000\([1, \infty)\)
nskipinteger100\([0, \infty)\)
printeveryinteger100\([0, \infty)\)
keepeveryinteger1\([1, \infty)\)
keeptrainfitslogicalTRUETRUE, FALSE-
usequantslogicalFALSETRUE, FALSE-
numcutinteger100\([1, \infty)\)
printcutoffsinteger0\((-\infty, \infty)\)
verboselogicalTRUETRUE, FALSE-
keeptreeslogicalFALSETRUE, FALSE-
keepcalllogicalTRUETRUE, FALSE-
sampleronlylogicalFALSETRUE, FALSE-
seedintegerNA\((-\infty, \infty)\)
proposalprobsuntyped-
splitprobsuntyped-

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.

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

Author

ck37

Super classes

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

Methods

Inherited methods


Method new()

Creates a new instance of this R6 class.

Usage


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.

Examples

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

# available parameters:
learner$param_set$ids()
#>  [1] "ntree"         "sigest"        "sigdf"         "sigquant"     
#>  [5] "k"             "power"         "base"          "ndpost"       
#>  [9] "nskip"         "printevery"    "keepevery"     "keeptrainfits"
#> [13] "usequants"     "numcut"        "printcutoffs"  "verbose"      
#> [17] "keeptrees"     "keepcall"      "sampleronly"   "seed"         
#> [21] "proposalprobs" "splitprobs"