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, RWeka
#> * Predict Types: [response]
#> * Feature Types: logical, integer, numeric, factor, ordered
#> * Properties: missings
# 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$model)
#>
#> RandomTree
#> ==========
#>
#> disp < 192.5
#> | wt < 2.26
#> | | qsec < 17.61 : 26 (1/0)
#> | | qsec >= 17.61
#> | | | drat < 4.5 : 32.4 (1/0)
#> | | | drat >= 4.5 : 30.4 (1/0)
#> | wt >= 2.26
#> | | hp < 96
#> | | | drat < 3.77 : 24.4 (1/0)
#> | | | drat >= 3.77 : 22.8 (2/0)
#> | | hp >= 96
#> | | | carb < 5
#> | | | | disp < 140.5 : 21.45 (2/0)
#> | | | | disp >= 140.5 : 21 (2/0)
#> | | | carb >= 5 : 19.7 (1/0)
#> disp >= 192.5
#> | wt < 5.38
#> | | carb < 2.5
#> | | | qsec < 16.95 : 15.5 (1/0)
#> | | | qsec >= 16.95
#> | | | | wt < 3.33 : 21.4 (1/0)
#> | | | | wt >= 3.33
#> | | | | | carb < 1.5 : 18.1 (1/0)
#> | | | | | carb >= 1.5
#> | | | | | | disp < 380 : 18.7 (1/0)
#> | | | | | | disp >= 380 : 19.2 (1/0)
#> | | carb >= 2.5
#> | | | disp < 395.5
#> | | | | wt < 3.75
#> | | | | | am < 0.5 : 17.3 (1/0)
#> | | | | | am >= 0.5 : 15.8 (1/0)
#> | | | | wt >= 3.75 : 15.2 (1/0)
#> | | | disp >= 395.5 : 14.7 (1/0)
#> | wt >= 5.38 : 10.4 (1/0)
#>
#> Size of the tree : 35
# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)
# Score the predictions
predictions$score()
#> regr.mse
#> 9.684545