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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("regr.decision_stump")

Meta Information

  • Task type: “regr”

  • Predict Types: “response”

  • 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::LearnerRegr -> LearnerRegrDecisionStump

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

LearnerRegrDecisionStump$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Define the Learner
learner = mlr3::lrn("regr.decision_stump")
print(learner)
#> <LearnerRegrDecisionStump:regr.decision_stump>: Decision Stump
#> * 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)
#> Decision Stump
#> 
#> Classifications
#> 
#> wt <= 2.3325 : 30.03333333333333
#> wt > 2.3325 : 18.194444444444443
#> wt is missing : 19.885714285714283
#> 


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

# Score the predictions
predictions$score()
#> regr.mse 
#> 23.49143