mlr_learners_regr.cforest: Regression Conditional Random Forest Learner

In mlr-org/mlr3extralearners: Extra Learners For mlr3

Regression Conditional Random Forest Learner

Description

A random forest based on conditional inference trees (ctree). Calls partykit::cforest() from partykit.

Dictionary

This Learner can be instantiated via lrn():

lrn("regr.cforest")

Meta Information

• Task type: “regr”

• Predict Types: “response”

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

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

Parameters

Id	Type	Default	Levels	Range
ntree	integer	500		[1, \infty)
replace	logical	FALSE	TRUE, FALSE	-
fraction	numeric	0.632		[0, 1]
mtry	integer	-		[0, \infty)
mtryratio	numeric	-		[0, 1]
applyfun	untyped	-		-
cores	integer	NULL		(-\infty, \infty)
trace	logical	FALSE	TRUE, FALSE	-
offset	untyped	-		-
cluster	untyped	-		-
scores	untyped	-		-
teststat	character	quadratic	quadratic, maximum	-
splitstat	character	quadratic	quadratic, maximum	-
splittest	logical	FALSE	TRUE, FALSE	-
testtype	character	Univariate	Bonferroni, MonteCarlo, Univariate, Teststatistic	-
nmax	untyped	-		-
pargs	untyped	-		-
alpha	numeric	0.05		[0, 1]
mincriterion	numeric	0		[0, 1]
logmincriterion	numeric	0		(-\infty, \infty)
minsplit	integer	20		[1, \infty)
minbucket	integer	7		[1, \infty)
minprob	numeric	0.01		[0, 1]
stump	logical	FALSE	TRUE, FALSE	-
lookahead	logical	FALSE	TRUE, FALSE	-
MIA	logical	FALSE	TRUE, FALSE	-
maxvar	integer	-		[1, \infty)
nresample	integer	9999		[1, \infty)
tol	numeric	1.490116e-08		[0, \infty)
maxsurrogate	integer	0		[0, \infty)
numsurrogate	logical	FALSE	TRUE, FALSE	-
maxdepth	integer	Inf		[0, \infty)
multiway	logical	FALSE	TRUE, FALSE	-
splittry	integer	2		[0, \infty)
intersplit	logical	FALSE	TRUE, FALSE	-
majority	logical	FALSE	TRUE, FALSE	-
caseweights	logical	TRUE	TRUE, FALSE	-
saveinfo	logical	FALSE	TRUE, FALSE	-
update	logical	FALSE	TRUE, FALSE	-
splitflavour	character	ctree	ctree, exhaustive	-
OOB	logical	FALSE	TRUE, FALSE	-
simplify	logical	TRUE	TRUE, FALSE	-
scale	logical	TRUE	TRUE, FALSE	-
nperm	integer	1		[0, \infty)
risk	character	loglik	loglik, misclassification	-
conditional	logical	FALSE	TRUE, FALSE	-
threshold	numeric	0.2		(-\infty, \infty)

Custom mlr3 parameters

• mtry:

  • This hyperparameter can alternatively be set via the added hyperparameter mtryratio as mtry = max(ceiling(mtryratio * n_features), 1). Note that mtry and mtryratio are mutually exclusive.

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrCForest

Methods

Public methods

• LearnerRegrCForest$new()

• LearnerRegrCForest$oob_error()

• LearnerRegrCForest$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

LearnerRegrCForest$new()

---

Method oob_error()

The out-of-bag error, calculated using the OOB predictions from partykit.

Usage

LearnerRegrCForest$oob_error()

Returns

numeric(1).

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerRegrCForest$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

# Define the Learner
learner = mlr3::lrn("regr.cforest")
print(learner)

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

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

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

print(learner$model)


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

# Score the predictions
predictions$score()

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