Classification Decision Stump Learner
mlr_learners_classif.decision_stump.Rd
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
Parameters
Id | Type | Default | Levels | Range |
subset | untyped | - | - | |
na.action | untyped | - | - | |
output_debug_info | logical | FALSE | TRUE, FALSE | - |
do_not_check_capabilities | logical | FALSE | TRUE, FALSE | - |
num_decimal_places | integer | 2 | \([1, \infty)\) | |
batch_size | integer | 100 | \([1, \infty)\) | |
options | untyped | NULL | - |
See also
as.data.table(mlr_learners)
for a table of available Learners in the running session (depending on the loaded packages).Chapter in the mlr3book: https://mlr3book.mlr-org.com/basics.html#learners
mlr3learners for a selection of recommended learners.
mlr3cluster for unsupervised clustering learners.
mlr3pipelines to combine learners with pre- and postprocessing steps.
mlr3tuning for tuning of hyperparameters, mlr3tuningspaces for established default tuning spaces.
Super classes
mlr3::Learner
-> mlr3::LearnerClassif
-> LearnerClassifDecisionStump
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