Regression Random Planted Forest Learner
mlr_learners_regr.rpf.Rd
Random Planted Forest: A directly interpretable tree ensemble.
Calls randomPlantedForest::rpf()
from 'randomPlantedForest'.
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
asmax_interaction = max(ceiling(max_interaction_ratio * n_features), 1)
. The parametermax_interaction_limit
can optionally be set as an upper bound, such thatmax_interaction_ratio * min(n_features, max_interaction_limit)
is used instead. This is analogous tomtry.ratio
inclassif.ranger
, withmax_interaction_limit
as an additional constraint. The parametermax_interaction_limit
is initialized toInf
.
Installation
Package 'randomPlantedForest' is not on CRAN and has to be installed from GitHub via
remotes::install_github("PlantedML/randomPlantedForest")
.
References
Hiabu, Munir, Mammen, Enno, Meyer, T. J (2023). “Random Planted Forest: a directly interpretable tree ensemble.” arXiv preprint arXiv:2012.14563. doi:10.48550/ARXIV.2012.14563 .
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::LearnerRegr
-> LearnerRegrRandomPlantedForest
Examples
# Define the Learner
learner = mlr3::lrn("regr.rpf")
print(learner)
#> <LearnerRegrRandomPlantedForest:regr.rpf>: Random Planted Forest
#> * Model: -
#> * Parameters: max_interaction_limit=Inf
#> * Packages: mlr3, randomPlantedForest
#> * Predict Types: [response]
#> * Feature Types: logical, integer, numeric, factor, ordered
#> * Properties: -
# 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$model)
#> -- Regression Random Planted Forest --
#>
#> Formula: NULL
#> Fit using 10 predictors and main effects only.
#> Forest is _not_ purified!
#>
#> Called with parameters:
#>
#> loss: L2
#> ntrees: 50
#> max_interaction: 1
#> splits: 30
#> split_try: 10
#> t_try: 0.4
#> delta: 0
#> epsilon: 0.1
#> deterministic: FALSE
#> nthreads: 1
#> purify: FALSE
#> cv: FALSE
# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)
# Score the predictions
predictions$score()
#> regr.mse
#> 2.26242