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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
#> ==========
#> 
#> 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