Classification BART (Bayesian Additive Regression Trees) Learner

Calls dbarts::bart from package 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")

Traits

• Packages: dbarts

• Predict Types: response, prob

• Feature Types: integer, numeric, factor, ordered

• Properties: twoclass, weights

See also

Dictionary of Learners: mlr3::mlr_learners

Author

ck37

Super classes

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

Methods

Public methods

• LearnerClassifBart$new()

• LearnerClassifBart$clone()

Inherited methods

• mlr3::Learner$format()

• mlr3::Learner$help()

• mlr3::Learner$predict()

• mlr3::Learner$predict_newdata()

• mlr3::Learner$print()

• mlr3::Learner$reset()

• mlr3::Learner$train()

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Method new()

Creates a new instance of this R6 class.

Usage

LearnerClassifBart$new()

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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

# stop example failing with warning if package not installed
learner = suppressWarnings(mlr3::lrn("classif.bart"))
print(learner)
#> <LearnerClassifBart:classif.bart>
#> * Model: -
#> * Parameters: list()
#> * Packages: 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] "offset"        "offset.test"