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/basics.html#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()

---

Method new()

Creates a new instance of this R6 class.

Usage

LearnerClassifRandomForestSRC$new()

---

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$model)
#>                          Sample size: 139
#>            Frequency of class labels: M=78, R=61
#>                      Number of trees: 500
#>            Forest terminal node size: 1
#>        Average no. of terminal nodes: 16.756
#> No. of variables tried at each split: 8
#>               Total no. of variables: 60
#>        Resampling used to grow trees: swor
#>     Resample size used to grow trees: 88
#>                             Analysis: RF-C
#>                               Family: class
#>                       Splitting rule: gini *random*
#>        Number of random split points: 10
#>                     Imbalanced ratio: 1.2787
#>                    (OOB) Brier score: 0.1276609
#>         (OOB) Normalized Brier score: 0.51064358
#>                            (OOB) AUC: 0.93022278
#>                       (OOB) Log-loss: 0.41151847
#>                         (OOB) PR-AUC: 0.91439222
#>                         (OOB) G-mean: 0.81223825
#>    (OOB) Requested performance error: 0.16546763, 0.06410256, 0.29508197
#> 
#> Confusion matrix:
#> 
#>           predicted
#>   observed  M  R class.error
#>          M 73  5      0.0641
#>          R 18 43      0.2951
#> 
#>       (OOB) Misclassification rate: 0.1654676
#> 
#> Random-classifier baselines (uniform):
#>    Brier: 0.25   Normalized Brier: 1   Log-loss: 0.69314718
print(learner$importance())
#>           V11           V12           V48            V9           V47 
#>  0.0517911833  0.0484819552  0.0454445011  0.0383480010  0.0368381044 
#>           V49           V36           V13           V10           V39 
#>  0.0339968362  0.0304566953  0.0303333958  0.0287306538  0.0211612111 
#>           V28           V37           V52           V34           V17 
#>  0.0186257351  0.0181561779  0.0172395775  0.0156589317  0.0152211000 
#>           V15           V18           V45           V21           V30 
#>  0.0143358197  0.0139519465  0.0137724118  0.0135309933  0.0134083756 
#>           V46           V16           V51           V35           V59 
#>  0.0129522050  0.0126409711  0.0111781759  0.0111591796  0.0110445608 
#>            V5           V20           V27           V14           V29 
#>  0.0110170367  0.0107471603  0.0092968416  0.0082845908  0.0079744939 
#>           V31           V23           V57           V26           V32 
#>  0.0078613036  0.0078305163  0.0075419180  0.0069505727  0.0068215632 
#>            V6            V3           V33            V4           V19 
#>  0.0066616520  0.0060987605  0.0059509242  0.0058037578  0.0056948924 
#>           V22           V54           V56           V55            V8 
#>  0.0050888259  0.0049351875  0.0038047368  0.0037890885  0.0037588086 
#>           V38           V42           V24           V41            V7 
#>  0.0037534418  0.0030468359  0.0030458402  0.0024871981  0.0024799327 
#>           V40           V25            V2           V60            V1 
#>  0.0023221353  0.0023028447  0.0017519807  0.0010060745  0.0008674028 
#>           V44           V50           V43           V58           V53 
#>  0.0007289310  0.0004347508  0.0003056321  0.0003003113 -0.0005966681 

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

# Score the predictions
predictions$score()
#> classif.ce 
#>  0.2173913