BlockForest Classification Learner

Random forests for blocks of clinical and omics covariate data. Calls blockForest::blockfor() from package blockForest.

In this learner, only the trained forest object ($forest) is retained. The optimized block-specific tuning parameters (paramvalues) and the biased OOB error estimate (biased_oob_error_donotuse) are discarded, as they are either not needed for downstream use or not reliable for performance estimation.

Initial parameter values

• num.threads is initialized to 1 to avoid conflicts with parallelization via future.

Dictionary

This Learner can be instantiated via lrn():

lrn("classif.blockforest")

Meta Information

• Task type: “classif”

• Predict Types: “response”, “prob”

• Feature Types: “logical”, “integer”, “numeric”, “factor”, “ordered”

• Required Packages: mlr3, mlr3extralearners, blockForest

Parameters

Id	Type	Default	Levels	Range
blocks	untyped	-		-
block.method	character	BlockForest	BlockForest, RandomBlock, BlockVarSel, VarProb, SplitWeights	-
num.trees	integer	2000		\([1, \infty)\)
mtry	untyped	NULL		-
nsets	integer	300		\([1, \infty)\)
num.trees.pre	integer	1500		\([1, \infty)\)
splitrule	character	extratrees	extratrees, gini	-
always.select.block	integer	0		\([0, 1]\)
importance	character	-	none, impurity, impurity_corrected, permutation	-
num.threads	integer	-		\([1, \infty)\)
seed	integer	NULL		\((-\infty, \infty)\)
verbose	logical	TRUE	TRUE, FALSE	-

References

Hornung, R., Wright, N. M (2019). “Block Forests: Random forests for blocks of clinical and omics covariate data.” BMC Bioinformatics, 20(1), 1–17. doi:10.1186/s12859-019-2942-y , https://doi.org/10.1186/s12859-019-2942-y.

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

bblodfon

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifBlockForest

Methods

Public methods

• LearnerClassifBlockForest$new()

• LearnerClassifBlockForest$importance()

• LearnerClassifBlockForest$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$reset()
• mlr3::Learner$selected_features()
• mlr3::Learner$train()
• mlr3::LearnerClassif$predict_newdata_fast()

---

Method new()

Creates a new instance of this R6 class.

Usage

LearnerClassifBlockForest$new()

---

Method importance()

The importance scores are extracted from the model slot variable.importance.

Usage

LearnerClassifBlockForest$importance()

Returns

Named numeric().

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClassifBlockForest$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Define a Task
task = tsk("sonar")
# Create train and test set
ids = partition(task)
# check task's features
task$feature_names
#>  [1] "V1"  "V10" "V11" "V12" "V13" "V14" "V15" "V16" "V17" "V18" "V19" "V2" 
#> [13] "V20" "V21" "V22" "V23" "V24" "V25" "V26" "V27" "V28" "V29" "V3"  "V30"
#> [25] "V31" "V32" "V33" "V34" "V35" "V36" "V37" "V38" "V39" "V4"  "V40" "V41"
#> [37] "V42" "V43" "V44" "V45" "V46" "V47" "V48" "V49" "V5"  "V50" "V51" "V52"
#> [49] "V53" "V54" "V55" "V56" "V57" "V58" "V59" "V6"  "V60" "V7"  "V8"  "V9" 
# partition features to 2 blocks
blocks = list(bl1 = 1:42, bl2 = 43:60)
# define learner
learner = lrn("classif.blockforest", blocks = blocks,
              importance = "permutation", nsets = 10, predict_type = "prob",
              num.trees = 50, num.trees.pre = 10, splitrule = "gini")
# Train the learner on the training ids
learner$train(task, row_ids = ids$train)
# feature importance
learner$importance()
#>           V11           V10           V12            V9           V48 
#>  2.007128e-02  1.411729e-02  1.206166e-02  1.123112e-02  9.725881e-03 
#>            V4           V47           V45           V16           V35 
#>  7.358149e-03  6.520847e-03  5.849192e-03  5.710182e-03  5.122188e-03 
#>           V51           V21           V15            V6            V8 
#>  4.728236e-03  4.601349e-03  4.303741e-03  4.233767e-03  4.131522e-03 
#>           V20           V28           V29           V37           V22 
#>  3.802191e-03  3.737910e-03  3.582095e-03  3.384041e-03  3.012218e-03 
#>           V36           V49           V31            V5           V52 
#>  2.919827e-03  2.718692e-03  2.438072e-03  2.279069e-03  2.267894e-03 
#>           V27           V25           V43           V46           V13 
#>  2.214828e-03  2.190153e-03  2.113948e-03  1.999545e-03  1.677667e-03 
#>           V33            V7           V40           V44           V58 
#>  1.676083e-03  1.502669e-03  1.376004e-03  1.312580e-03  1.305250e-03 
#>           V23           V24           V53            V3           V60 
#>  1.000658e-03  9.931478e-04  9.722796e-04  7.437815e-04  6.611196e-04 
#>           V14           V41           V18           V59           V19 
#>  5.729742e-04  4.187138e-04  4.047785e-04  3.953082e-04  3.078718e-04 
#>            V1            V2           V42           V17           V32 
#>  1.897631e-04  4.912238e-06  0.000000e+00 -9.982269e-06 -6.056371e-05 
#>           V30           V54           V50           V34           V26 
#> -8.558355e-05 -2.210958e-04 -3.137006e-04 -4.598775e-04 -7.185999e-04 
#>           V38           V57           V56           V39           V55 
#> -9.445704e-04 -1.531917e-03 -2.798605e-03 -3.032471e-03 -3.487564e-03 
# Make predictions for the test observations
pred = learner$predict(task, row_ids = ids$test)
pred
#> 
#> ── <PredictionClassif> for 69 observations: ────────────────────────────────────
#>  row_ids truth response    prob.M    prob.R
#>        4     R        M 0.5686508 0.4313492
#>       10     R        R 0.2389683 0.7610317
#>       13     R        R 0.3135000 0.6865000
#>      ---   ---      ---       ---       ---
#>      198     M        M 0.8247540 0.1752460
#>      200     M        M 0.7944444 0.2055556
#>      206     M        M 0.7860000 0.2140000
# Score the predictions
pred$score()
#> classif.ce 
#>  0.2028986