Classification Decision Tree Learner
mlr_learners_classif.reptree.Rd
Fast decision tree learner.
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
Parameters
Id | Type | Default | Levels | Range |
subset | untyped | - | - | |
na.action | untyped | - | - | |
M | integer | 2 | \((-\infty, \infty)\) | |
V | numeric | 0.001 | \((-\infty, \infty)\) | |
N | integer | 3 | \((-\infty, \infty)\) | |
S | integer | 1 | \((-\infty, \infty)\) | |
P | logical | - | TRUE, FALSE | - |
L | integer | -1 | \((-\infty, \infty)\) | |
I | integer | 0 | \((-\infty, \infty)\) | |
R | logical | FALSE | TRUE, FALSE | - |
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
-> LearnerClassifREPTree
Examples
# Define the Learner
learner = mlr3::lrn("classif.reptree")
print(learner)
#>
#> ── <LearnerClassifREPTree> (classif.reptree): Decision Tree Learner ────────────
#> • 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$model)
#>
#> REPTree
#> ============
#>
#> V11 < 0.2
#> | V1 < 0.04 : R (39/4) [15/3]
#> | V1 >= 0.04 : M (4/0) [1/0]
#> V11 >= 0.2
#> | V23 < 0.78
#> | | V45 < 0.25
#> | | | V36 < 0.14 : M (6/2) [3/0]
#> | | | V36 >= 0.14 : R (9/0) [11/4]
#> | | V45 >= 0.25 : M (12/0) [8/0]
#> | V23 >= 0.78 : M (22/0) [9/5]
#>
#> Size of the tree : 11
# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)
# Score the predictions
predictions$score()
#> classif.ce
#> 0.2898551