Calls partykit::cforest from package partykit.

Dictionary

This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn():

mlr_learners$get("classif.cforest")
lrn("classif.cforest")

Traits

  • Packages: partykit, sandwich, coin

  • Predict Types: response, prob

  • Feature Types: integer, numeric, factor, ordered

  • Properties: multiclass, oob_error, twoclass, weights

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

See also

Author

sumny

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifCForest

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage

LearnerClassifCForest$new()


Method oob_error()

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

Usage

LearnerClassifCForest$oob_error()

Returns

numeric(1).


Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClassifCForest$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# stop example failing with warning if package not installed learner = suppressWarnings(mlr3::lrn("classif.cforest")) print(learner)
#> <LearnerClassifCForest:classif.cforest> #> * Model: - #> * Parameters: teststat=quadratic, testtype=Univariate, mincriterion=0, #> saveinfo=FALSE #> * Packages: partykit, sandwich, coin #> * Predict Type: response #> * Feature types: integer, numeric, factor, ordered #> * Properties: multiclass, oob_error, twoclass, weights
# available parameters: learner$param_set$ids()
#> [1] "ntree" "replace" "fraction" "mtry" #> [5] "applyfun" "cores" "trace" "offset" #> [9] "cluster" "scores" "teststat" "splitstat" #> [13] "splittest" "testtype" "nmax" "pargs" #> [17] "alpha" "mincriterion" "logmincriterion" "minsplit" #> [21] "minbucket" "minprob" "stump" "lookahead" #> [25] "MIA" "nresample" "tol" "maxsurrogate" #> [29] "numsurrogate" "maxdepth" "multiway" "splittry" #> [33] "intersplit" "majority" "caseweights" "saveinfo" #> [37] "update" "splitflavour" "maxvar" "OOB" #> [41] "simplify" "scale" "nperm" "risk" #> [45] "conditional" "threshold"