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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

Dictionary

This Learner can be instantiated via lrn():

lrn("regr.random_tree")

Meta Information

  • Task type: “regr”

  • Predict Types: “response”

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

  • Required Packages: mlr3, RWeka

Parameters

IdTypeDefaultLevelsRange
subsetuntyped--
na.actionuntyped--
Kinteger0\([0, \infty)\)
Minteger1\([1, \infty)\)
Vnumeric0.001\((-\infty, \infty)\)
Sinteger1\((-\infty, \infty)\)
depthinteger0\([0, \infty)\)
Ninteger0\([0, \infty)\)
UlogicalFALSETRUE, FALSE-
BlogicalFALSETRUE, FALSE-
output_debug_infologicalFALSETRUE, FALSE-
do_not_check_capabilitieslogicalFALSETRUE, FALSE-
num_decimal_placesinteger2\([1, \infty)\)
batch_sizeinteger100\([1, \infty)\)
optionsuntypedNULL-

See also

Author

damirpolat

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrRandomTree

Methods

Inherited methods


Method new()

Creates a new instance of this R6 class.

Usage


Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerRegrRandomTree$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

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
#> ==========
#> 
#> hp < 116.5
#> |   disp < 93.5
#> |   |   carb < 1.5
#> |   |   |   qsec < 19.18 : 27.3 (1/0)
#> |   |   |   qsec >= 19.18 : 32.4 (1/0)
#> |   |   carb >= 1.5 : 30.4 (1/0)
#> |   disp >= 93.5
#> |   |   drat < 4.27
#> |   |   |   hp < 96
#> |   |   |   |   wt < 3.17 : 22.8 (2/0)
#> |   |   |   |   wt >= 3.17 : 24.4 (1/0)
#> |   |   |   hp >= 96
#> |   |   |   |   hp < 109.5 : 21.45 (2/0)
#> |   |   |   |   hp >= 109.5 : 21 (1/0)
#> |   |   drat >= 4.27 : 26 (1/0)
#> hp >= 116.5
#> |   disp < 450
#> |   |   vs < 0.5
#> |   |   |   drat < 3.16
#> |   |   |   |   drat < 3.08
#> |   |   |   |   |   qsec < 17.8
#> |   |   |   |   |   |   drat < 2.92 : 15.5 (1/0)
#> |   |   |   |   |   |   drat >= 2.92 : 17.3 (1/0)
#> |   |   |   |   |   qsec >= 17.8 : 15.2 (1/0)
#> |   |   |   |   drat >= 3.08 : 19.2 (1/0)
#> |   |   |   drat >= 3.16
#> |   |   |   |   disp < 325.5 : 15 (1/0)
#> |   |   |   |   disp >= 325.5
#> |   |   |   |   |   drat < 3.48 : 14.7 (1/0)
#> |   |   |   |   |   drat >= 3.48 : 13.3 (1/0)
#> |   |   vs >= 0.5
#> |   |   |   qsec < 18.6 : 19.2 (1/0)
#> |   |   |   qsec >= 18.6 : 17.8 (1/0)
#> |   disp >= 450 : 10.4 (2/0)
#> 
#> 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 
#> 13.41386