Classification Model-based Recursive Partitioning Learner

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

Dictionary

This Learner can be instantiated via lrn():

lrn("classif.mob")

Meta Information

• Task type: “classif”

• Predict Types: “response”, “prob”

• 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	-		-

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. 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.

Author

sumny

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifMob

Methods

Public methods

• LearnerClassifMob$new()

• LearnerClassifMob$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$train()
• mlr3::LearnerClassif$predict_newdata_fast()

---

Method new()

Creates a new instance of this R6 class.

Usage

LearnerClassifMob$new()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClassifMob$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

library(mlr3)
logit_ = function(y, x, start = NULL, weights = NULL, offset = NULL, ...) {
  glm(y ~ 1, family = binomial, start = start, ...)
}
learner = LearnerClassifMob$new()
learner$param_set$values$rhs = "."
learner$param_set$values$fit = logit_
learner$param_set$values$additional = list(maxit = 100)
learner$feature_types = c("logical", "integer", "numeric", "factor", "ordered")
learner$properties = c("twoclass", "weights")

predict_fun = function(object, newdata, task, .type) {
  p = unname(predict(object, newdata = newdata, type = "response"))
  levs = task$levels(task$target_names)[[1L]]

  if (.type == "response") {
    ifelse(p < 0.5, levs[1L], levs[2L])
  } else {
    y = matrix(c(1 - p, p), ncol = 2L, nrow = length(p))
    colnames(y) = levs
    y
  }
}
task = tsk("breast_cancer")
learner$param_set$values$predict_fun = predict_fun
ids = partition(task)
learner$train(task, row_ids = ids$train)
learner$predict(task, row_ids = ids$test)
#> 
#> ── <PredictionClassif> for 225 observations: ───────────────────────────────────
#>  row_ids     truth  response
#>        4    benign malignant
#>        5    benign malignant
#>        7    benign    benign
#>      ---       ---       ---
#>      678    benign malignant
#>      680    benign malignant
#>      683 malignant malignant