Regression Gradient Boosting Learner

Regression gradient boosting learner. Calls bst::bst() from bst.

Initial parameter values

• Learner = "ls": Default base learner type

• xval = 0: No cross-validation

• maxdepth = 1: Maximum tree depth

Dictionary

This Learner can be instantiated via lrn():

lrn("regr.bst")

Meta Information

• Task type: “regr”

• Predict Types: “response”

• Feature Types: “numeric”

• Required Packages: mlr3, mlr3extralearners, bst, rpart

Parameters

Id	Type	Default	Levels	Range
center	logical	FALSE	TRUE, FALSE	-
coefir	untyped	NULL		-
cost	numeric	0.5		\([0, 1]\)
cp	numeric	0.01		\([0, 1]\)
df	integer	4		\([1, \infty)\)
family	character	gaussian	gaussian, laplace, huber, rhuberDC, thingeDC, tbinomDC, binomdDC	-
f.init	untyped	NULL		-
fk	untyped	NULL		-
intercept	logical	TRUE	TRUE, FALSE	-
iter	integer	1		\([1, \infty)\)
Learner	character	ls	ls, sm, tree	-
maxdepth	integer	1		\([1, 30]\)
maxsurrogate	integer	5		\([0, \infty)\)
minbucket	integer	-		\([1, \infty)\)
minsplit	integer	20		\([1, \infty)\)
mstop	integer	50		\([1, \infty)\)
numsample	integer	50		\([1, \infty)\)
nu	numeric	0.1		\([0, 1]\)
q	numeric	-		\([0, 1]\)
qh	numeric	-		\([0, 1]\)
s	numeric	-		\([0, \infty)\)
sh	numeric	-		\([0, \infty)\)
start	logical	FALSE	TRUE, FALSE	-
surrogatestyle	integer	0		\([0, 1]\)
threshold	character	adaptive	adaptive, fixed	-
trace	logical	FALSE	TRUE, FALSE	-
trun	logical	FALSE	TRUE, FALSE	-
twinboost	logical	FALSE	TRUE, FALSE	-
twintype	integer	1		\([1, 2]\)
xselect.init	untyped	NULL		-
xval	integer	10		\([0, \infty)\)

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

annanzrv

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrBst

Methods

Public methods

• LearnerRegrBst$new()

• LearnerRegrBst$clone()

Inherited methods

• mlr3::Learner$base_learner()
• mlr3::Learner$configure()
• mlr3::Learner$encapsulate()
• mlr3::Learner$format()
• mlr3::Learner$help()
• mlr3::Learner$predict()
• mlr3::Learner$predict_newdata()
• mlr3::Learner$print()
• mlr3::Learner$reset()
• mlr3::Learner$selected_features()
• mlr3::Learner$train()
• mlr3::LearnerRegr$predict_newdata_fast()

---

Method new()

Creates a new instance of this R6 class.

Usage

LearnerRegrBst$new()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerRegrBst$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Define the Learner
learner = lrn("regr.bst")
print(learner)
#> 
#> ── <LearnerRegrBst> (regr.bst): Gradient Boosting ──────────────────────────────
#> • Model: -
#> • Parameters: Learner=ls, maxdepth=1, xval=0
#> • Packages: mlr3, mlr3extralearners, bst, and rpart
#> • Predict Types: [response]
#> • Feature Types: numeric
#> • Encapsulation: none (fallback: -)
#> • Properties:
#> • Other settings: use_weights = 'error'

# Define a Task
task = tsk("mtcars")

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

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)
#> 
#> 	 Models Fitted with Gradient Boosting
#> 
#> Call:
#> bst::bst(x = data[, features, with = FALSE], y = data[[target]],     ctrl = ctrl, control.tree = ctrl_tree, learner = pars$Learner)
#> 
#> [1] "gaussian"
#> 
#> Base learner:  ls 
#> Number of boosting iterations: mstop = 50 
#> Step size:  0.1 
#> Offset:  20.3619 
#> 
#> Coefficients: 
#>           am         carb          cyl         disp         drat         gear 
#>  3.373246439 -0.298168952  0.000000000 -0.009836173  0.000000000  0.000000000 
#>           hp         qsec           vs           wt 
#>  0.000000000  0.000000000  4.570354486  0.000000000 
#> attr(,"offset")
#> [1] 20.3619
#> 


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

# Score the predictions
predictions$score()
#> regr.mse 
#> 7.544578