Classification Random Forest SRC Learner

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

Id	Type	Default	Levels	Range
ntree	integer	500		\([1, \infty)\)
mtry	integer	-		\([1, \infty)\)
mtry.ratio	numeric	-		\([0, 1]\)
nodesize	integer	15		\([1, \infty)\)
nodedepth	integer	-		\([1, \infty)\)
splitrule	character	gini	gini, auc, entropy	-
nsplit	integer	10		\([0, \infty)\)
importance	character	FALSE	FALSE, TRUE, none, permute, random, anti	-
block.size	integer	10		\([1, \infty)\)
bootstrap	character	by.root	by.root, by.node, none, by.user	-
samptype	character	swor	swor, swr	-
samp	untyped	-		-
membership	logical	FALSE	TRUE, FALSE	-
sampsize	untyped	-		-
sampsize.ratio	numeric	-		\([0, 1]\)
na.action	character	na.omit	na.omit, na.impute	-
nimpute	integer	1		\([1, \infty)\)
proximity	character	FALSE	FALSE, TRUE, inbag, oob, all	-
distance	character	FALSE	FALSE, TRUE, inbag, oob, all	-
forest.wt	character	FALSE	FALSE, TRUE, inbag, oob, all	-
xvar.wt	untyped	-		-
split.wt	untyped	-		-
forest	logical	TRUE	TRUE, FALSE	-
var.used	character	FALSE	FALSE, all.trees	-
split.depth	character	FALSE	FALSE, all.trees, by.tree	-
seed	integer	-		\((-\infty, -1]\)
do.trace	logical	FALSE	TRUE, FALSE	-
get.tree	untyped	-		-
outcome	character	train	train, test	-
ptn.count	integer	0		\([0, \infty)\)
cores	integer	1		\([1, \infty)\)
save.memory	logical	FALSE	TRUE, FALSE	-
perf.type	character	-	gmean, misclass, brier, none	-
case.depth	logical	FALSE	TRUE, FALSE	-
marginal.xvar	untyped	NULL		-

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

• 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/chapters/chapter2/data_and_basic_modeling.html#sec-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

RaphaelS1

Super classes

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

Methods

Public methods

• LearnerClassifRandomForestSRC$new()

• LearnerClassifRandomForestSRC$importance()

• LearnerClassifRandomForestSRC$selected_features()

• LearnerClassifRandomForestSRC$oob_error()

• LearnerClassifRandomForestSRC$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$reset()
• mlr3::Learner$train()
• mlr3::LearnerClassif$predict_newdata_fast()

---

LearnerClassifRandomForestSRC$new()

Creates a new instance of this R6 class.

Usage

LearnerClassifRandomForestSRC$new()

---

LearnerClassifRandomForestSRC$importance()

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

Usage

LearnerClassifRandomForestSRC$importance()

Returns

Named numeric().

---

LearnerClassifRandomForestSRC$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().

---

LearnerClassifRandomForestSRC$oob_error()

OOB error extracted from the model slot err.rate.

Usage

LearnerClassifRandomForestSRC$oob_error()

Returns

numeric().

---

LearnerClassifRandomForestSRC$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', predict_raw = 'FALSE'

# 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$model)
#>                          Sample size: 139
#>            Frequency of class labels: M=73, R=66
#>                      Number of trees: 500
#>            Forest terminal node size: 1
#>        Average no. of terminal nodes: 16.778
#> No. of variables tried at each split: 8
#>               Total no. of variables: 60
#>        Resampling used to grow trees: swor
#>     Resample size used to grow trees: 88
#>                             Analysis: RF-C
#>                               Family: class
#>                       Splitting rule: gini *random*
#>        Number of random split points: 10
#>                     Imbalanced ratio: 1.1061
#>                    (OOB) Brier score: 0.13805003
#>         (OOB) Normalized Brier score: 0.55220011
#>                            (OOB) AUC: 0.90971357
#>                       (OOB) Log-loss: 0.43658389
#>                         (OOB) PR-AUC: 0.90224393
#>                         (OOB) G-mean: 0.79224133
#>    (OOB) Requested performance error: 0.20143885, 0.1369863, 0.27272727
#> 
#> Confusion matrix:
#> 
#>           predicted
#>   observed  M  R class.error
#>          M 63 10      0.1370
#>          R 18 48      0.2727
#> 
#>       (OOB) Misclassification rate: 0.2014388
#> 
#> Random-classifier baselines (uniform):
#>    Brier: 0.25   Normalized Brier: 1   Log-loss: 0.69314718
print(learner$importance())
#>           V11           V10           V12            V9           V36 
#>  9.104496e-02  5.002222e-02  4.960539e-02  3.050644e-02  2.863931e-02 
#>           V48           V51           V28           V17           V45 
#>  2.783171e-02  2.262111e-02  2.247842e-02  2.138015e-02  1.978581e-02 
#>            V8           V27           V13           V39           V18 
#>  1.802014e-02  1.760007e-02  1.585464e-02  1.571938e-02  1.570030e-02 
#>            V6            V5           V30           V35           V52 
#>  1.540693e-02  1.452612e-02  1.353451e-02  1.252646e-02  1.222836e-02 
#>           V16           V19           V46           V32           V47 
#>  1.222377e-02  1.178500e-02  1.177774e-02  1.174463e-02  1.092390e-02 
#>           V33           V37            V1           V34           V29 
#>  1.079690e-02  1.016751e-02  9.610001e-03  9.300966e-03  8.875921e-03 
#>           V44           V21           V49            V4           V15 
#>  7.409488e-03  7.278236e-03  7.226348e-03  6.989485e-03  6.680434e-03 
#>           V54           V40           V60           V20           V14 
#>  5.977187e-03  5.810945e-03  5.666405e-03  5.661770e-03  5.392952e-03 
#>           V56           V22           V42           V43           V25 
#>  5.113906e-03  5.093682e-03  5.090679e-03  4.800775e-03  4.502305e-03 
#>            V3           V38           V26           V58            V7 
#>  4.381942e-03  4.352381e-03  4.227415e-03  3.942928e-03  3.934559e-03 
#>           V53           V31           V59           V23           V55 
#>  3.357772e-03  3.348173e-03  3.054196e-03  2.624813e-03  1.445448e-03 
#>           V24           V57            V2           V41           V50 
#>  1.170903e-03  1.147874e-03  5.807670e-04  9.583024e-06 -1.502324e-04 

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

# Score the predictions
predictions$score()
#> classif.ce 
#>  0.1594203