BlockForest Regression Learner

Random forests for blocks of clinical and omics covariate data. Calls blockForest::blockfor() from package blockForest. The training model includes only the $forest slot, excluding the paramvalues and the biased_oob_error_donotuse.

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("regr.blockforest")

Meta Information

• Task type: “regr”

• Predict Types: “response”, “se”

• 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, variance, maxstat	-
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	-
se.method	character	infjack	jack, infjack	-

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::LearnerRegr -> LearnerRegrBlockForest

Methods

Public methods

• LearnerRegrBlockForest$new()

• LearnerRegrBlockForest$importance()

• LearnerRegrBlockForest$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$train()
• mlr3::LearnerRegr$predict_newdata_fast()

---

Method new()

Creates a new instance of this R6 class.

Usage

LearnerRegrBlockForest$new()

---

Method importance()

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

Usage

LearnerRegrBlockForest$importance()

Returns

Named numeric().

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerRegrBlockForest$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Define a Task
task = tsk("mtcars")
# Create train and test set
ids = partition(task)
# check task's features
task$feature_names
#>  [1] "am"   "carb" "cyl"  "disp" "drat" "gear" "hp"   "qsec" "vs"   "wt"  
# partition features to 2 blocks
blocks = list(bl1 = 1:3, bl2 = 4:10)
# define learner
learner = lrn("regr.blockforest", blocks = blocks,
              importance = "permutation", nsets = 10,
              num.trees = 50, num.trees.pre = 10, splitrule = "variance")
# Train the learner on the training ids
learner$train(task, row_ids = ids$train)
# feature importance
learner$importance()
#>        cyl       disp         hp       carb       drat         wt         am 
#> 10.0025764  8.9265352  8.8083551  5.7826888  4.2889854  2.9902400  1.6221734 
#>       qsec         vs       gear 
#>  1.6084609  1.5795716  0.3770831 
# Make predictions for the test observations
pred = learner$predict(task, row_ids = ids$test)
pred
#> 
#> ── <PredictionRegr> for 11 observations: ───────────────────────────────────────
#>  row_ids truth response
#>        4  21.4 21.77513
#>        6  18.1 21.48042
#>        8  24.4 23.81629
#>      ---   ---      ---
#>       28  30.4 23.92569
#>       29  15.8 17.11762
#>       32  21.4 24.69356
# Score the predictions
pred$score()
#> regr.mse 
#> 8.120491