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		\([1, \infty)\)
ntime	integer	-		\([1, \infty)\)
cause	integer	-		\([1, \infty)\)
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, by.tree	-
split.depth	character	FALSE	FALSE, all.trees, by.tree	-
seed	integer	-		\((-\infty, -1]\)
do.trace	logical	FALSE	TRUE, FALSE	-
statistics	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	-

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()

---

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.

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 = mlr3::lrn("classif.rfsrc", importance = "TRUE")
print(learner)
#> <LearnerClassifRandomForestSRC:classif.rfsrc>: Random Forest
#> * Model: -
#> * Parameters: importance=TRUE
#> * Packages: mlr3, mlr3extralearners, randomForestSRC
#> * Predict Types:  [response], prob
#> * Feature Types: logical, integer, numeric, factor
#> * Properties: importance, missings, multiclass, oob_error, twoclass,
#>   weights

# Define a Task
task = mlr3::tsk("sonar")
# Create train and test set
ids = mlr3::partition(task)

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

print(learner$model)
#>                          Sample size: 139
#>            Frequency of class labels: 75, 64
#>                      Number of trees: 500
#>            Forest terminal node size: 1
#>        Average no. of terminal nodes: 17.296
#> No. of variables tried at each split: 8
#>               Total no. of variables: 60
#>        Resampling used to grow trees: swor
#>     Resample size used to grow trees: 88
#>                             Analysis: RF-C
#>                               Family: class
#>                       Splitting rule: gini *random*
#>        Number of random split points: 10
#>                     Imbalanced ratio: 1.1719
#>                    (OOB) Brier score: 0.13843574
#>         (OOB) Normalized Brier score: 0.55374296
#>                            (OOB) AUC: 0.9140625
#>                       (OOB) Log-loss: 0.44033095
#>                         (OOB) PR-AUC: 0.90454586
#>                         (OOB) G-mean: 0.77325071
#>    (OOB) Requested performance error: 0.20143885, 0.06666667, 0.359375
#> 
#> Confusion matrix:
#> 
#>           predicted
#>   observed  M  R class.error
#>          M 70  5      0.0667
#>          R 23 41      0.3594
#> 
#>       (OOB) Misclassification rate: 0.2014388
print(learner$importance())
#>          V12          V11          V52          V39           V9          V45 
#> 0.0992211995 0.0634239028 0.0562007124 0.0366942928 0.0345611663 0.0263170945 
#>          V37          V10          V36          V20           V4           V5 
#> 0.0253063850 0.0215177989 0.0196548806 0.0166010294 0.0164865828 0.0164398287 
#>          V33          V27          V51          V46          V48          V15 
#> 0.0157676203 0.0157565299 0.0148772106 0.0147477010 0.0140280853 0.0139569879 
#>          V16          V29           V6          V47          V28          V21 
#> 0.0135477997 0.0122346899 0.0116427927 0.0111869016 0.0109420899 0.0109371108 
#>          V49          V31          V22          V19          V17          V13 
#> 0.0107896978 0.0105112479 0.0104838787 0.0104816517 0.0104553421 0.0103473082 
#>          V40          V18          V30          V32          V53          V35 
#> 0.0102900374 0.0100916173 0.0090388497 0.0081683690 0.0078921576 0.0076877244 
#>          V43          V54          V59          V24          V14           V8 
#> 0.0074490832 0.0073969931 0.0072710355 0.0069926613 0.0064450301 0.0062566334 
#>          V26          V50          V60          V41          V44          V23 
#> 0.0062554033 0.0056818865 0.0052549456 0.0052524872 0.0050875056 0.0049403233 
#>          V34           V3           V7           V1          V25          V57 
#> 0.0039821050 0.0038286375 0.0037999304 0.0037924942 0.0033643700 0.0024830103 
#>          V56          V42          V55           V2          V58          V38 
#> 0.0021930946 0.0018863307 0.0012053167 0.0010081902 0.0008609440 0.0004497023 

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

# Score the predictions
predictions$score()
#> classif.ce 
#>   0.173913