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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("classif.random_tree")

Meta Information

  • Task type: “classif”

  • Predict Types: “response”, “prob”

  • 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::LearnerClassif -> LearnerClassifRandomTree

Active bindings

marshaled

(logical(1))
Whether the learner has been marshaled.

Methods

Inherited methods


Method new()

Creates a new instance of this R6 class.


Method marshal()

Marshal the learner's model.

Usage

LearnerClassifRandomTree$marshal(...)

Arguments

...

(any)
Additional arguments passed to mlr3::marshal_model().


Method unmarshal()

Unmarshal the learner's model.

Usage

LearnerClassifRandomTree$unmarshal(...)

Arguments

...

(any)
Additional arguments passed to mlr3::unmarshal_model().


Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClassifRandomTree$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Define the Learner
learner = lrn("classif.random_tree")
print(learner)
#> 
#> ── <LearnerClassifRandomTree> (classif.random_tree): Random Tree ───────────────
#> • Model: -
#> • Parameters: list()
#> • Packages: mlr3 and RWeka
#> • Predict Types: [response] and prob
#> • Feature Types: logical, integer, numeric, factor, and ordered
#> • Encapsulation: none (fallback: -)
#> • Properties: marshal, missings, multiclass, and twoclass
#> • Other settings: use_weights = 'error'

# Define a Task
task = tsk("sonar")

# Create train and test set
ids = partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)
#> 
#> RandomTree
#> ==========
#> 
#> V48 < 0.08
#> |   V27 < 0.9
#> |   |   V21 < 0.55 : R (28/0)
#> |   |   V21 >= 0.55
#> |   |   |   V14 < 0.56
#> |   |   |   |   V33 < 0.29 : R (7/0)
#> |   |   |   |   V33 >= 0.29
#> |   |   |   |   |   V60 < 0.01
#> |   |   |   |   |   |   V27 < 0.23 : M (2/0)
#> |   |   |   |   |   |   V27 >= 0.23
#> |   |   |   |   |   |   |   V32 < 0.3
#> |   |   |   |   |   |   |   |   V40 < 0.38 : M (2/0)
#> |   |   |   |   |   |   |   |   V40 >= 0.38 : R (1/0)
#> |   |   |   |   |   |   |   V32 >= 0.3 : R (7/0)
#> |   |   |   |   |   V60 >= 0.01 : M (5/0)
#> |   |   |   V14 >= 0.56 : M (5/0)
#> |   V27 >= 0.9
#> |   |   V3 < 0.03
#> |   |   |   V23 < 0.74 : R (4/0)
#> |   |   |   V23 >= 0.74
#> |   |   |   |   V43 < 0.15 : R (2/0)
#> |   |   |   |   V43 >= 0.15 : M (5/0)
#> |   |   V3 >= 0.03 : M (5/0)
#> V48 >= 0.08
#> |   V10 < 0.09
#> |   |   V23 < 0.5 : R (8/0)
#> |   |   V23 >= 0.5
#> |   |   |   V34 < 0.51
#> |   |   |   |   V27 < 0.95 : M (3/0)
#> |   |   |   |   V27 >= 0.95 : R (1/0)
#> |   |   |   V34 >= 0.51 : R (2/0)
#> |   V10 >= 0.09
#> |   |   V18 < 0.41
#> |   |   |   V28 < 0.26 : R (1/0)
#> |   |   |   V28 >= 0.26 : M (29/0)
#> |   |   V18 >= 0.41
#> |   |   |   V25 < 0.95
#> |   |   |   |   V22 < 0.58 : M (3/0)
#> |   |   |   |   V22 >= 0.58
#> |   |   |   |   |   V15 < 0.79
#> |   |   |   |   |   |   V26 < 0.39 : M (1/0)
#> |   |   |   |   |   |   V26 >= 0.39 : R (10/0)
#> |   |   |   |   |   V15 >= 0.79 : M (1/0)
#> |   |   |   V25 >= 0.95 : M (7/0)
#> 
#> Size of the tree : 45


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()
#> classif.ce 
#>  0.3188406