Regression Random Forest Learner from Weka
mlr_learners_regr.random_forest_weka.Rd
Class for constructing a forest of random trees.
Calls RWeka::make_Weka_classifier()
from RWeka.
Custom mlr3 parameters
output_debug_info
:original id: output-debug-info
do_not_check_capabilities
:original id: do-not-check-capabilities
num_decimal_places
:original id: num-decimal-places
batch_size
:original id: batch-size
store_out_of_bag_predictions
:original id: store-out-of-bag-predictions
output_out_of_bag_complexity_statistics
:original id: output-out-of-bag-complexity-statistics
num_slots
:original id: num-slots
Reason for change: This learner contains changed ids of the following control arguments since their ids contain irregular pattern
attribute-importance
removed:Compute and output attribute importance (mean impurity decrease method)
Reason for change: The parameter is removed because it's unclear how to actually use it.
Parameters
Id | Type | Default | Levels | Range |
subset | untyped | - | - | |
na.action | untyped | - | - | |
P | numeric | 100 | \([0, 100]\) | |
O | logical | FALSE | TRUE, FALSE | - |
store_out_of_bag_predictions | logical | FALSE | TRUE, FALSE | - |
output_out_of_bag_complexity_statistics | logical | FALSE | TRUE, FALSE | - |
logical | FALSE | TRUE, FALSE | - | |
I | integer | 100 | \([1, \infty)\) | |
num_slots | integer | 1 | \((-\infty, \infty)\) | |
K | integer | 0 | \((-\infty, \infty)\) | |
M | integer | 1 | \([1, \infty)\) | |
V | numeric | 0.001 | \((-\infty, \infty)\) | |
S | integer | 1 | \((-\infty, \infty)\) | |
depth | integer | 0 | \([0, \infty)\) | |
N | integer | 0 | \((-\infty, \infty)\) | |
U | logical | FALSE | TRUE, FALSE | - |
B | logical | FALSE | TRUE, FALSE | - |
output_debug_info | logical | FALSE | TRUE, FALSE | - |
do_not_check_capabilities | logical | FALSE | TRUE, FALSE | - |
num_decimal_places | integer | 2 | \([1, \infty)\) | |
batch_size | integer | 100 | \([1, \infty)\) | |
options | untyped | NULL | - |
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::LearnerRegr
-> LearnerRegrRandomForestWeka
Examples
# Define the Learner
learner = mlr3::lrn("regr.random_forest_weka")
print(learner)
#> <LearnerRegrRandomForestWeka:regr.random_forest_weka>: Random Forest
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, RWeka
#> * 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$model)
#> RandomForest
#>
#> Bagging with 100 iterations and base learner
#>
#> weka.classifiers.trees.RandomTree -K 0 -M 1.0 -V 0.001 -S 1 -do-not-check-capabilities
# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)
# Score the predictions
predictions$score()
#> regr.mse
#> 4.296442