Regression Conditional Random Forest Learner

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("regr.cforest")
lrn("regr.cforest")

Traits

• Packages: partykit, sandwich, coin

• Predict Types: response

• Feature Types: integer, numeric, factor, ordered

• Properties: oob_error, 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

Dictionary of Learners: mlr3::mlr_learners

Author

sumny

Super classes

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

Methods

Public methods

• LearnerRegrCForest$new()

• LearnerRegrCForest$oob_error()

• LearnerRegrCForest$clone()

Inherited methods

• mlr3::Learner$format()

• mlr3::Learner$help()

• mlr3::Learner$predict()

• mlr3::Learner$predict_newdata()

• mlr3::Learner$print()

• mlr3::Learner$reset()

• mlr3::Learner$train()

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Method new()

Creates a new instance of this R6 class.

Usage

LearnerRegrCForest$new()

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Method oob_error()

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

Usage

LearnerRegrCForest$oob_error()

Returns

numeric(1).

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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.

Examples

# stop example failing with warning if package not installed
learner = suppressWarnings(mlr3::lrn("regr.cforest"))
print(learner)
#> <LearnerRegrCForest:regr.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: oob_error, 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"             "maxvar"          "nresample"       "tol"            
#> [29] "maxsurrogate"    "numsurrogate"    "maxdepth"        "multiway"       
#> [33] "splittry"        "intersplit"      "majority"        "caseweights"    
#> [37] "saveinfo"        "update"          "splitflavour"    "OOB"            
#> [41] "simplify"        "scale"           "nperm"           "risk"           
#> [45] "conditional"     "threshold"