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 lrn():

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	FALSE	TRUE, FALSE	-
nthread	integer	1		\((-\infty, \infty)\)
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	-

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: 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$base_learner()
• mlr3::Learner$encapsulate()
• 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

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

# Define a Task
task = mlr3::tsk("sonar")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)
#> 
#> Running BART with binary y
#> 
#> number of trees: 200
#> number of chains: 1, number of threads 1
#> tree thinning rate: 1
#> Prior:
#> 	k prior fixed to 2.000000
#> 	power and base for tree prior: 2.000000 0.950000
#> 	use quantiles for rule cut points: false
#> 	proposal probabilities: birth/death 0.50, swap 0.10, change 0.40; birth 0.50
#> data:
#> 	number of training observations: 139
#> 	number of test observations: 0
#> 	number of explanatory variables: 60
#> 
#> Cutoff rules c in x<=c vs x>c
#> Number of cutoffs: (var: number of possible c):
#> (1: 100) (2: 100) (3: 100) (4: 100) (5: 100) 
#> (6: 100) (7: 100) (8: 100) (9: 100) (10: 100) 
#> (11: 100) (12: 100) (13: 100) (14: 100) (15: 100) 
#> (16: 100) (17: 100) (18: 100) (19: 100) (20: 100) 
#> (21: 100) (22: 100) (23: 100) (24: 100) (25: 100) 
#> (26: 100) (27: 100) (28: 100) (29: 100) (30: 100) 
#> (31: 100) (32: 100) (33: 100) (34: 100) (35: 100) 
#> (36: 100) (37: 100) (38: 100) (39: 100) (40: 100) 
#> (41: 100) (42: 100) (43: 100) (44: 100) (45: 100) 
#> (46: 100) (47: 100) (48: 100) (49: 100) (50: 100) 
#> (51: 100) (52: 100) (53: 100) (54: 100) (55: 100) 
#> (56: 100) (57: 100) (58: 100) (59: 100) (60: 100) 
#> 
#> offsets:
#> 	reg : 0.00 0.00 0.00 0.00 0.00
#> Running mcmc loop:
#> iteration: 100 (of 1000)
#> iteration: 200 (of 1000)
#> iteration: 300 (of 1000)
#> iteration: 400 (of 1000)
#> iteration: 500 (of 1000)
#> iteration: 600 (of 1000)
#> iteration: 700 (of 1000)
#> iteration: 800 (of 1000)
#> iteration: 900 (of 1000)
#> iteration: 1000 (of 1000)
#> total seconds in loop: 0.521236
#> 
#> Tree sizes, last iteration:
#> [1] 2 2 2 4 2 2 3 2 3 3 3 2 2 3 2 2 4 3 
#> 2 2 2 2 2 3 2 2 2 2 2 2 2 2 1 2 4 2 3 2 
#> 5 1 2 2 3 2 3 3 2 2 1 2 4 3 2 4 2 5 2 2 
#> 2 2 1 1 2 4 3 2 2 2 2 2 2 3 2 2 2 2 2 1 
#> 4 2 2 2 2 3 2 2 2 2 2 2 3 2 2 2 2 2 2 2 
#> 1 2 2 3 3 2 2 3 5 2 2 4 2 2 3 1 5 2 3 2 
#> 3 2 2 2 3 1 2 2 2 2 2 5 2 2 4 3 2 2 2 1 
#> 3 2 2 5 3 3 3 1 2 2 2 3 3 3 3 3 2 4 2 2 
#> 2 3 2 2 3 2 4 2 3 2 2 2 2 2 1 3 3 2 1 2 
#> 2 2 2 2 2 3 3 2 2 2 3 3 4 2 1 3 1 4 2 2 
#> 2 3 
#> 
#> Variable Usage, last iteration (var:count):
#> (1: 3) (2: 5) (3: 7) (4: 8) (5: 4) 
#> (6: 8) (7: 3) (8: 10) (9: 5) (10: 5) 
#> (11: 3) (12: 3) (13: 5) (14: 12) (15: 5) 
#> (16: 4) (17: 5) (18: 3) (19: 1) (20: 5) 
#> (21: 5) (22: 5) (23: 6) (24: 3) (25: 2) 
#> (26: 5) (27: 8) (28: 3) (29: 4) (30: 7) 
#> (31: 5) (32: 1) (33: 3) (34: 3) (35: 2) 
#> (36: 4) (37: 4) (38: 2) (39: 4) (40: 4) 
#> (41: 6) (42: 7) (43: 3) (44: 3) (45: 2) 
#> (46: 6) (47: 4) (48: 2) (49: 4) (50: 7) 
#> (51: 6) (52: 2) (53: 4) (54: 5) (55: 4) 
#> (56: 4) (57: 1) (58: 5) (59: 8) (60: 6) 
#> 
#> DONE BART
#> 

print(learner$model)
#> 
#> Call:
#> dbarts::bart(x.train = x_train, y.train = y_train, keeptrees = TRUE)
#> 


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()
#> classif.ce 
#>   0.173913