Classification Random Forest SRC Learner
Source:R/learner_randomForestSRC_classif_rfsrc.R
mlr_learners_classif.rfsrc.Rd
Random forest for classification.
Calls randomForestSRC::rfsrc()
from randomForestSRC.
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 hyperparametermtry.ratio
asmtry = max(ceiling(mtry.ratio * n_features), 1)
. Note thatmtry
andmtry.ratio
are mutually exclusive.sampsize
: This hyperparameter can alternatively be set via the added hyperparametersampsize.ratio
assampsize = max(ceiling(sampsize.ratio * n_obs), 1)
. Note thatsampsize
andsampsize.ratio
are mutually exclusive.cores
: This value is set as the optionrf.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
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.
Super classes
mlr3::Learner
-> mlr3::LearnerClassif
-> LearnerClassifRandomForestSRC
Methods
Inherited methods
Method importance()
The importance scores are extracted from the model slot importance
, returned for
'all'.
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.
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