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Decision Stump Learner. Calls RWeka::DecisionStump() 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.decision_stump")

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--
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 -> LearnerClassifDecisionStump

Methods

Inherited methods


Method new()

Creates a new instance of this R6 class.


Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClassifDecisionStump$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

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

# Define a Task
task = mlr3::tsk("sonar")

# 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)
#> Decision Stump
#> 
#> Classifications
#> 
#> V11 <= 0.17070000000000002 : R
#> V11 > 0.17070000000000002 : M
#> V11 is missing : M
#> 
#> Class distributions
#> 
#> V11 <= 0.17070000000000002
#> M	R	
#> 0.16981132075471697	0.8301886792452831	
#> V11 > 0.17070000000000002
#> M	R	
#> 0.7906976744186046	0.20930232558139536	
#> V11 is missing
#> M	R	
#> 0.5539568345323741	0.4460431654676259	
#> 


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

# Score the predictions
predictions$score()
#> classif.ce 
#>  0.3478261