Classification Random Tree Learner
Source:R/learner_RWeka_classif_random_tree.R
mlr_learners_classif.random_tree.RdTree 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
Parameters
| Id | Type | Default | Levels | Range |
| subset | untyped | - | - | |
| na.action | untyped | - | - | |
| K | integer | 0 | \([0, \infty)\) | |
| M | integer | 1 | \([1, \infty)\) | |
| V | numeric | 0.001 | \((-\infty, \infty)\) | |
| S | integer | 1 | \((-\infty, \infty)\) | |
| depth | integer | 0 | \([0, \infty)\) | |
| N | integer | 0 | \([0, \infty)\) | |
| U | logical | FALSE | TRUE, FALSE | - |
| B | 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 -> LearnerClassifRandomTree
Methods
Inherited methods
mlr3::Learner$base_learner()mlr3::Learner$configure()mlr3::Learner$encapsulate()mlr3::Learner$format()mlr3::Learner$help()mlr3::Learner$predict()mlr3::Learner$predict_newdata()mlr3::Learner$print()mlr3::Learner$reset()mlr3::Learner$selected_features()mlr3::Learner$train()mlr3::LearnerClassif$predict_newdata_fast()
Method marshal()
Marshal the learner's model.
Arguments
...(any)
Additional arguments passed tomlr3::marshal_model().
Method unmarshal()
Unmarshal the learner's model.
Arguments
...(any)
Additional arguments passed tomlr3::unmarshal_model().
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$model)
#>
#> RandomTree
#> ==========
#>
#> V12 < 0.2
#> | V16 < 0.36 : R (31/0)
#> | V16 >= 0.36
#> | | V8 < 0.07 : R (5/0)
#> | | V8 >= 0.07
#> | | | V37 < 0.46
#> | | | | V45 < 0.14
#> | | | | | V39 < 0.19 : R (2/0)
#> | | | | | V39 >= 0.19 : M (1/0)
#> | | | | V45 >= 0.14 : M (8/0)
#> | | | V37 >= 0.46
#> | | | | V20 < 0.69 : R (5/0)
#> | | | | V20 >= 0.69 : M (1/0)
#> V12 >= 0.2
#> | V47 < 0.08
#> | | V36 < 0.12 : M (5/0)
#> | | V36 >= 0.12
#> | | | V26 < 0.16 : M (1/0)
#> | | | V26 >= 0.16
#> | | | | V52 < 0 : M (1/0)
#> | | | | V52 >= 0
#> | | | | | V57 < 0 : M (1/0)
#> | | | | | V57 >= 0 : R (13/0)
#> | V47 >= 0.08
#> | | V17 < 0.5
#> | | | V10 < 0.13
#> | | | | V33 < 0.49 : M (3/0)
#> | | | | V33 >= 0.49 : R (2/0)
#> | | | V10 >= 0.13 : M (42/0)
#> | | V17 >= 0.5
#> | | | V32 < 0.49
#> | | | | V46 < 0.16 : M (10/0)
#> | | | | V46 >= 0.16
#> | | | | | V24 < 0.79 : R (3/0)
#> | | | | | V24 >= 0.79 : M (2/0)
#> | | | V32 >= 0.49 : R (3/0)
#>
#> Size of the tree : 37
# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)
# Score the predictions
predictions$score()
#> classif.ce
#> 0.2028986