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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$model)
#>                          Sample size: 139
#>            Frequency of class labels: M=75, R=64
#>                      Number of trees: 500
#>            Forest terminal node size: 1
#>        Average no. of terminal nodes: 17.168
#> No. of variables tried at each split: 8
#>               Total no. of variables: 60
#>        Resampling used to grow trees: swor
#>     Resample size used to grow trees: 88
#>                             Analysis: RF-C
#>                               Family: class
#>                       Splitting rule: gini *random*
#>        Number of random split points: 10
#>                     Imbalanced ratio: 1.1719
#>                    (OOB) Brier score: 0.13649055
#>         (OOB) Normalized Brier score: 0.54596218
#>                            (OOB) AUC: 0.92260417
#>                       (OOB) Log-loss: 0.43221282
#>                         (OOB) PR-AUC: 0.90786716
#>                         (OOB) G-mean: 0.73908727
#>    (OOB) Requested performance error: 0.23021583, 0.08, 0.40625
#> 
#> Confusion matrix:
#> 
#>           predicted
#>   observed  M  R class.error
#>          M 69  6      0.0800
#>          R 26 38      0.4062
#> 
#>       (OOB) Misclassification rate: 0.2302158
#> 
#> Random-classifier baselines (uniform):
#>    Brier: 0.25   Normalized Brier: 1   Log-loss: 0.69314718
print(learner$importance())
#>          V12           V9          V11          V10          V36          V48 
#> 0.0640557480 0.0566155973 0.0557530585 0.0358038146 0.0294256574 0.0272924846 
#>          V49          V17           V5          V47          V27          V16 
#> 0.0253902455 0.0247943570 0.0235911553 0.0228401597 0.0218924515 0.0215296448 
#>          V28          V52          V15          V37          V45           V6 
#> 0.0212257422 0.0202662771 0.0196168969 0.0188036324 0.0171366104 0.0170399295 
#>          V51          V46          V24          V18          V23          V44 
#> 0.0170309863 0.0152328387 0.0142766627 0.0141253941 0.0132207155 0.0126381838 
#>          V59          V13          V31          V25          V14          V22 
#> 0.0125123199 0.0123664954 0.0121991451 0.0113663907 0.0106317692 0.0104656202 
#>          V58          V53          V39          V38          V35          V19 
#> 0.0101803811 0.0100480210 0.0100403462 0.0097340048 0.0097001322 0.0094561415 
#>           V4          V26          V60          V20           V2           V3 
#> 0.0093660343 0.0092179023 0.0088455420 0.0087532288 0.0085833525 0.0073910004 
#>          V34          V40          V32          V30           V8          V57 
#> 0.0071176074 0.0066768577 0.0062680244 0.0062442700 0.0055465278 0.0055443948 
#>           V1          V21          V54          V33          V43          V41 
#> 0.0052207192 0.0050630309 0.0046478376 0.0046430484 0.0045017133 0.0043746676 
#>          V55          V50          V29          V42           V7          V56 
#> 0.0041955008 0.0040610565 0.0036295588 0.0033578693 0.0022059080 0.0009933635 

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

# Score the predictions
predictions$score()
#> classif.ce 
#>   0.115942