Classification Random Tree Learner
Source:R/learner_RWeka_classif_random_tree.R
mlr_learners_classif.random_tree.Rd
Tree 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$model)
#>
#> RandomTree
#> ==========
#>
#> V44 < 0.43
#> | V51 < 0.01
#> | | V21 < 0.56
#> | | | V4 < 0.05 : R (26/0)
#> | | | V4 >= 0.05
#> | | | | V48 < 0.09
#> | | | | | V56 < 0.01 : M (1/0)
#> | | | | | V56 >= 0.01 : R (3/0)
#> | | | | V48 >= 0.09 : M (2/0)
#> | | V21 >= 0.56
#> | | | V31 < 0.58
#> | | | | V23 < 0.8
#> | | | | | V40 < 0.33
#> | | | | | | V27 < 0.98 : R (7/0)
#> | | | | | | V27 >= 0.98 : M (1/0)
#> | | | | | V40 >= 0.33
#> | | | | | | V25 < 0.81 : M (2/0)
#> | | | | | | V25 >= 0.81 : R (1/0)
#> | | | | V23 >= 0.8
#> | | | | | V58 < 0 : R (2/0)
#> | | | | | V58 >= 0
#> | | | | | | V28 < 0.9 : M (10/0)
#> | | | | | | V28 >= 0.9
#> | | | | | | | V11 < 0.21 : R (1/0)
#> | | | | | | | V11 >= 0.21 : M (1/0)
#> | | | V31 >= 0.58
#> | | | | V15 < 0.07 : M (1/0)
#> | | | | V15 >= 0.07 : R (8/0)
#> | V51 >= 0.01
#> | | V20 < 0.48
#> | | | V16 < 0.46
#> | | | | V4 < 0.05
#> | | | | | V34 < 0.48
#> | | | | | | V10 < 0.14 : R (1/0)
#> | | | | | | V10 >= 0.14 : M (5/0)
#> | | | | | V34 >= 0.48 : R (6/0)
#> | | | | V4 >= 0.05 : M (7/0)
#> | | | V16 >= 0.46 : R (8/0)
#> | | V20 >= 0.48
#> | | | V49 < 0.05
#> | | | | V3 < 0.01 : R (2/0)
#> | | | | V3 >= 0.01
#> | | | | | V41 < 0.3 : M (8/0)
#> | | | | | V41 >= 0.3
#> | | | | | | V56 < 0.01 : R (2/0)
#> | | | | | | V56 >= 0.01 : M (1/0)
#> | | | V49 >= 0.05 : M (16/0)
#> V44 >= 0.43
#> | V44 < 0.68 : M (16/0)
#> | V44 >= 0.68 : R (1/0)
#>
#> Size of the tree : 51
# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)
# Score the predictions
predictions$score()
#> classif.ce
#> 0.2753623