mlr_learners_regr.rpf: Regression Random Planted Forest Learner

In mlr-org/mlr3extralearners: Extra Learners For mlr3

Regression Random Planted Forest Learner

Description

Random Planted Forest: A directly interpretable tree ensemble.

Calls randomPlantedForest::rpf() from 'randomPlantedForest'.

Dictionary

This Learner can be instantiated via lrn():

lrn("regr.rpf")

Meta Information

• Task type: “regr”

• Predict Types: “response”

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

• Required Packages: mlr3, randomPlantedForest

Parameters

Id	Type	Default	Levels	Range
max_interaction	integer	1		[0, \infty)
max_interaction_ratio	numeric	-		[0, 1]
max_interaction_limit	integer	-		[1, \infty)
ntrees	integer	50		[1, \infty)
splits	integer	30		[1, \infty)
split_try	integer	10		[1, \infty)
t_try	numeric	0.4		[0, 1]
deterministic	logical	FALSE	TRUE, FALSE	-
nthreads	integer	1		[1, \infty)
cv	logical	FALSE	TRUE, FALSE	-
purify	logical	FALSE	TRUE, FALSE	-

Custom mlr3 parameters

• max_interaction:

  • This hyperparameter can alternatively be set via max_interaction_ratio as max_interaction = max(ceiling(max_interaction_ratio * n_features), 1). The parameter max_interaction_limit can optionally be set as an upper bound, such that max_interaction_ratio * min(n_features, max_interaction_limit) is used instead. This is analogous to mtry.ratio in classif.ranger, with max_interaction_limit as an additional constraint. The parameter max_interaction_limit is initialized to Inf.

Installation

Package 'randomPlantedForest' is not on CRAN and has to be installed from GitHub via remotes::install_github("PlantedML/randomPlantedForest").

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrRandomPlantedForest

Methods

Public methods

• LearnerRegrRandomPlantedForest$new()

• LearnerRegrRandomPlantedForest$clone()

Inherited methods

• mlr3::Learner$base_learner()
• mlr3::Learner$encapsulate()
• mlr3::Learner$format()
• mlr3::Learner$help()
• mlr3::Learner$predict()
• mlr3::Learner$predict_newdata()
• mlr3::Learner$print()
• mlr3::Learner$reset()
• mlr3::Learner$train()

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Method new()

Creates a new instance of this R6 class.

Usage

LearnerRegrRandomPlantedForest$new()

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Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerRegrRandomPlantedForest$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Author(s)

jemus42

References

Hiabu, Munir, Mammen, Enno, Meyer, T. J (2023). “Random Planted Forest: a directly interpretable tree ensemble.” arXiv preprint arXiv:2012.14563. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.48550/ARXIV.2012.14563")}.

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.

Examples

# Define the Learner
learner = mlr3::lrn("regr.rpf")
print(learner)

# Define a Task
task = mlr3::tsk("mtcars")

# 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)


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

# Score the predictions
predictions$score()

mlr-org/mlr3extralearners documentation built on Nov. 11, 2024, 11:11 a.m.