mlr_learners_regr.mob: Regression Model-based Recursive Partitioning Learner

In mlr-org/mlr3extralearners: Extra Learners For mlr3

Regression Model-based Recursive Partitioning Learner

Description

Model-based recursive partitioning algorithm. Calls partykit::mob() from mob.

Dictionary

This Learner can be instantiated via lrn():

lrn("regr.mob")

Meta Information

• Task type: “regr”

• Predict Types: “response”, “se”

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

• Required Packages: mlr3, mlr3extralearners, partykit, sandwich, coin

Parameters

Id	Type	Default	Levels	Range
rhs	untyped	-		-
fit	untyped	-		-
offset	untyped	-		-
cluster	untyped	-		-
alpha	numeric	0.05		[0, 1]
bonferroni	logical	TRUE	TRUE, FALSE	-
minsize	integer	-		[1, \infty)
minsplit	integer	-		[1, \infty)
minbucket	integer	-		[1, \infty)
maxdepth	integer	Inf		[0, \infty)
mtry	integer	Inf		[0, \infty)
trim	numeric	0.1		[0, \infty)
breakties	logical	FALSE	TRUE, FALSE	-
parm	untyped	-		-
dfsplit	integer	-		[0, \infty)
prune	untyped	-		-
restart	logical	TRUE	TRUE, FALSE	-
verbose	logical	FALSE	TRUE, FALSE	-
caseweights	logical	TRUE	TRUE, FALSE	-
ytype	character	vector	vector, matrix, data.frame	-
xtype	character	matrix	vector, matrix, data.frame	-
terminal	untyped	"object"		-
inner	untyped	"object"		-
model	logical	TRUE	TRUE, FALSE	-
numsplit	character	left	left, center	-
catsplit	character	binary	binary, multiway	-
vcov	character	opg	opg, info, sandwich	-
ordinal	character	chisq	chisq, max, L2	-
nrep	integer	10000		[0, \infty)
applyfun	untyped	-		-
cores	integer	NULL		(-\infty, \infty)
additional	untyped	-		-
predict_fun	untyped	-		-

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrMob

Methods

Public methods

• LearnerRegrMob$new()

• LearnerRegrMob$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

LearnerRegrMob$new()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerRegrMob$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Author(s)

sumny

References

Hothorn T, Zeileis A (2015). “partykit: A Modular Toolkit for Recursive Partytioning in R.” Journal of Machine Learning Research, 16(118), 3905-3909. http://jmlr.org/papers/v16/hothorn15a.html.

Hothorn T, Hornik K, Zeileis A (2006). “Unbiased Recursive Partitioning: A Conditional Inference Framework.” Journal of Computational and Graphical Statistics, 15(3), 651–674. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1198/106186006x133933")}, https://doi.org/10.1198/106186006x133933.

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.

Examples

library(mlr3)
lm_ = function(y, x, start = NULL, weights = NULL, offset = NULL, ...) {
  lm(y ~ 1, ...)
}
learner = LearnerRegrMob$new()
learner$param_set$values$rhs = "."
learner$param_set$values$fit = lm_
learner$feature_types = c("logical", "integer", "numeric", "factor", "ordered")

predict_fun = function(object, newdata, task, .type) {
  preds = predict(object, newdata = newdata, type = "response", se.fit = TRUE)
  cbind(preds$fit, preds$se.fit)
}
learner$param_set$values$predict_fun = predict_fun
task = tsk("mtcars")
ids = partition(task)
learner$train(task, row_ids = ids$train)
learner$predict(task, row_ids = ids$test)

mlr-org/mlr3extralearners documentation built on Nov. 11, 2024, 11:11 a.m.