Regression Random Tree Learner
mlr_learners_regr.random_tree.Rd
Tree that considers K randomly chosen attributes at each node.
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
Reason for change: This learner contains changed ids of the following control arguments since their ids contain irregular pattern
Parameters
Id | Type | Default | Levels | Range |
subset | untyped | - | - | |
na.action | untyped | - | - | |
K | integer | 0 | \([0, \infty)\) | |
M | integer | 1 | \([1, \infty)\) | |
V | numeric | 0.001 | \((-\infty, \infty)\) | |
S | integer | 1 | \((-\infty, \infty)\) | |
depth | integer | 0 | \([0, \infty)\) | |
N | integer | 0 | \([0, \infty)\) | |
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 | - |
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
-> LearnerRegrRandomTree
Examples
# Define the Learner
learner = mlr3::lrn("regr.random_tree")
print(learner)
#>
#> ── <LearnerRegrRandomTree> (regr.random_tree): Random Tree ─────────────────────
#> • Model: -
#> • Parameters: list()
#> • Packages: mlr3 and RWeka
#> • Predict Types: [response]
#> • Feature Types: logical, integer, numeric, factor, and ordered
#> • Encapsulation: none (fallback: -)
#> • Properties: missings
#> • Other settings: use_weights = 'error'
# 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$model)
#>
#> RandomTree
#> ==========
#>
#> disp < 163.8
#> | wt < 2.26
#> | | qsec < 16.8 : 26 (1/0)
#> | | qsec >= 16.8
#> | | | drat < 3.92 : 30.4 (1/0)
#> | | | drat >= 3.92
#> | | | | hp < 59 : 30.4 (1/0)
#> | | | | hp >= 59 : 32.4 (1/0)
#> | wt >= 2.26
#> | | carb < 3
#> | | | hp < 96 : 22.8 (2/0)
#> | | | hp >= 96 : 21.5 (1/0)
#> | | carb >= 3
#> | | | disp < 152.5 : 19.7 (1/0)
#> | | | disp >= 152.5 : 21 (2/0)
#> disp >= 163.8
#> | wt < 3.81
#> | | cyl < 7 : 17.95 (2/0.02)
#> | | cyl >= 7
#> | | | qsec < 14.55 : 15.8 (1/0)
#> | | | qsec >= 14.55
#> | | | | qsec < 16.57
#> | | | | | carb < 6 : 14.3 (1/0)
#> | | | | | carb >= 6 : 15 (1/0)
#> | | | | qsec >= 16.57 : 15.2 (2/0)
#> | wt >= 3.81
#> | | hp < 222.5 : 10.4 (2/0)
#> | | hp >= 222.5
#> | | | disp < 395 : 13.3 (1/0)
#> | | | disp >= 395 : 14.7 (1/0)
#>
#> Size of the tree : 31
# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)
# Score the predictions
predictions$score()
#> regr.mse
#> 15.86227