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Random forest for classification. Calls randomForestSRC::rfsrc() from randomForestSRC.

Dictionary

This Learner can be instantiated via lrn():

lrn("classif.rfsrc")

Meta Information

  • Task type: “classif”

  • Predict Types: “response”, “prob”

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

  • Required Packages: mlr3, mlr3extralearners, randomForestSRC

Parameters

IdTypeDefaultLevelsRange
ntreeinteger500\([1, \infty)\)
mtryinteger-\([1, \infty)\)
mtry.rationumeric-\([0, 1]\)
nodesizeinteger15\([1, \infty)\)
nodedepthinteger-\([1, \infty)\)
splitrulecharacterginigini, auc, entropy-
nsplitinteger10\([0, \infty)\)
importancecharacterFALSEFALSE, TRUE, none, permute, random, anti-
block.sizeinteger10\([1, \infty)\)
bootstrapcharacterby.rootby.root, by.node, none, by.user-
samptypecharactersworswor, swr-
sampuntyped--
membershiplogicalFALSETRUE, FALSE-
sampsizeuntyped--
sampsize.rationumeric-\([0, 1]\)
na.actioncharacterna.omitna.omit, na.impute-
nimputeinteger1\([1, \infty)\)
proximitycharacterFALSEFALSE, TRUE, inbag, oob, all-
distancecharacterFALSEFALSE, TRUE, inbag, oob, all-
forest.wtcharacterFALSEFALSE, TRUE, inbag, oob, all-
xvar.wtuntyped--
split.wtuntyped--
forestlogicalTRUETRUE, FALSE-
var.usedcharacterFALSEFALSE, all.trees-
split.depthcharacterFALSEFALSE, all.trees, by.tree-
seedinteger-\((-\infty, -1]\)
do.tracelogicalFALSETRUE, FALSE-
get.treeuntyped--
outcomecharactertraintrain, test-
ptn.countinteger0\([0, \infty)\)
coresinteger1\([1, \infty)\)
save.memorylogicalFALSETRUE, FALSE-
perf.typecharacter-gmean, misclass, brier, none-
case.depthlogicalFALSETRUE, FALSE-
marginal.xvaruntypedNULL-

Custom mlr3 parameters

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

  • sampsize: This hyperparameter can alternatively be set via the added hyperparameter sampsize.ratio as sampsize = max(ceiling(sampsize.ratio * n_obs), 1). Note that sampsize and sampsize.ratio are mutually exclusive.

  • cores: This value is set as the option rf.cores during training and is set to 1 by default.

References

Breiman, Leo (2001). “Random Forests.” Machine Learning, 45(1), 5–32. ISSN 1573-0565, doi:10.1023/A:1010933404324 .

See also

Author

RaphaelS1

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifRandomForestSRC

Methods

Inherited methods


Method new()

Creates a new instance of this R6 class.


Method importance()

The importance scores are extracted from the model slot importance, returned for 'all'.

Usage

LearnerClassifRandomForestSRC$importance()

Returns

Named numeric().


Method selected_features()

Selected features are extracted from the model slot var.used.

Note: Due to a known issue in randomForestSRC, enabling var.used = "all.trees" causes prediction to fail. Therefore, this setting should be used exclusively for feature selection purposes and not when prediction is required.

Usage

LearnerClassifRandomForestSRC$selected_features()

Returns

character().


Method oob_error()

OOB error extracted from the model slot err.rate.

Usage

LearnerClassifRandomForestSRC$oob_error()

Returns

numeric().


Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClassifRandomForestSRC$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Define the Learner
learner = lrn("classif.rfsrc", importance = "TRUE")
print(learner)
#> 
#> ── <LearnerClassifRandomForestSRC> (classif.rfsrc): Random Forest ──────────────
#> • Model: -
#> • Parameters: importance=TRUE
#> • Packages: mlr3, mlr3extralearners, and randomForestSRC
#> • Predict Types: [response] and prob
#> • Feature Types: logical, integer, numeric, and factor
#> • Encapsulation: none (fallback: -)
#> • Properties: importance, missings, multiclass, oob_error, selected_features,
#> twoclass, and weights
#> • Other settings: use_weights = 'use'

# Define a Task
task = tsk("sonar")
# Create train and test set
ids = partition(task)

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

print(learner$model)
#>                          Sample size: 139
#>            Frequency of class labels: M=73, R=66
#>                      Number of trees: 500
#>            Forest terminal node size: 1
#>        Average no. of terminal nodes: 16.604
#> No. of variables tried at each split: 8
#>               Total no. of variables: 60
#>        Resampling used to grow trees: swor
#>     Resample size used to grow trees: 88
#>                             Analysis: RF-C
#>                               Family: class
#>                       Splitting rule: gini *random*
#>        Number of random split points: 10
#>                     Imbalanced ratio: 1.1061
#>                    (OOB) Brier score: 0.12845873
#>         (OOB) Normalized Brier score: 0.51383494
#>                            (OOB) AUC: 0.93295973
#>                       (OOB) Log-loss: 0.41501287
#>                         (OOB) PR-AUC: 0.930151
#>                         (OOB) G-mean: 0.8377013
#>    (OOB) Requested performance error: 0.15107914, 0.05479452, 0.25757576
#> 
#> Confusion matrix:
#> 
#>           predicted
#>   observed  M  R class.error
#>          M 69  4      0.0548
#>          R 17 49      0.2576
#> 
#>       (OOB) Misclassification rate: 0.1510791
#> 
#> Random-classifier baselines (uniform):
#>    Brier: 0.25   Normalized Brier: 1   Log-loss: 0.69314718
print(learner$importance())
#>         V12          V9         V11         V49         V10         V48 
#> 0.085041646 0.072647368 0.072143976 0.063255261 0.033994172 0.031008839 
#>         V36         V21         V43         V20         V17         V16 
#> 0.026098203 0.024584410 0.023744110 0.023592072 0.023147190 0.022559314 
#>         V15         V52         V39         V37         V47          V5 
#> 0.018750342 0.018045842 0.017280514 0.016599834 0.015913979 0.015323069 
#>         V18         V44         V31         V38         V46         V45 
#> 0.015012397 0.013348525 0.013246292 0.012982233 0.012266132 0.012222961 
#>         V51          V7         V22          V4          V1         V14 
#> 0.011791959 0.011641558 0.011389851 0.011349414 0.011237577 0.010622953 
#>         V28         V58         V33          V3         V19         V57 
#> 0.009752209 0.008445492 0.008172757 0.008014824 0.007856965 0.007670962 
#>          V6         V23         V26         V29         V42         V27 
#> 0.007417173 0.007280726 0.006832949 0.006557987 0.006264226 0.006107288 
#>         V13         V30         V41         V53         V35         V25 
#> 0.005695780 0.005570424 0.005552004 0.005522209 0.005427536 0.004822125 
#>          V2         V24         V56         V59         V34          V8 
#> 0.004805925 0.004778512 0.004661375 0.003794872 0.003794002 0.003649698 
#>         V40         V60         V54         V32         V50         V55 
#> 0.003621039 0.003508256 0.003484591 0.003207805 0.002646954 0.002316895 

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

# Score the predictions
predictions$score()
#> classif.ce 
#>  0.1884058