Classification Random Tree Learner
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
Examples
# Define the Learner
learner = mlr3::lrn("classif.random_tree")
print(learner)
#> <LearnerClassifRandomTree:classif.random_tree>: Random Tree
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, RWeka
#> * Predict Types: [response], prob
#> * Feature Types: logical, integer, numeric, factor, ordered
#> * Properties: missings, multiclass, twoclass
# 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$model)
#>
#> RandomTree
#> ==========
#>
#> V2 < 0.06
#> | V11 < 0.22
#> | | V20 < 0.62
#> | | | V51 < 0.02
#> | | | | V28 < 0.96 : R (27/0)
#> | | | | V28 >= 0.96
#> | | | | | V13 < 0.26 : R (2/0)
#> | | | | | V13 >= 0.26 : M (1/0)
#> | | | V51 >= 0.02
#> | | | | V47 < 0.14 : R (4/0)
#> | | | | V47 >= 0.14
#> | | | | | V10 < 0.09 : R (1/0)
#> | | | | | V10 >= 0.09 : M (3/0)
#> | | V20 >= 0.62
#> | | | V39 < 0.27
#> | | | | V45 < 0.16 : R (9/0)
#> | | | | V45 >= 0.16 : M (3/0)
#> | | | V39 >= 0.27
#> | | | | V50 < 0.03
#> | | | | | V17 < 0.26 : R (1/0)
#> | | | | | V17 >= 0.26 : M (6/0)
#> | | | | V50 >= 0.03 : R (2/0)
#> | V11 >= 0.22
#> | | V18 < 0.86
#> | | | V57 < 0.01 : M (29/0)
#> | | | V57 >= 0.01
#> | | | | V27 < 0.82
#> | | | | | V49 < 0.05
#> | | | | | | V1 < 0.04 : R (4/0)
#> | | | | | | V1 >= 0.04
#> | | | | | | | V13 < 0.42 : M (1/0)
#> | | | | | | | V13 >= 0.42 : R (1/0)
#> | | | | | V49 >= 0.05
#> | | | | | | V16 < 0.62 : M (4/0)
#> | | | | | | V16 >= 0.62 : R (1/0)
#> | | | | V27 >= 0.82 : M (6/0)
#> | | V18 >= 0.86
#> | | | V20 < 0.88 : R (7/0)
#> | | | V20 >= 0.88
#> | | | | V10 < 0.15 : M (1/0)
#> | | | | V10 >= 0.15 : R (1/0)
#> V2 >= 0.06
#> | V58 < 0 : R (2/0)
#> | V58 >= 0
#> | | V19 < 0.22 : R (2/0)
#> | | V19 >= 0.22
#> | | | V53 < 0.01
#> | | | | V24 < 0.9 : M (3/0)
#> | | | | V24 >= 0.9 : R (1/0)
#> | | | V53 >= 0.01 : M (17/0)
#>
#> Size of the tree : 51
# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)
# Score the predictions
predictions$score()
#> classif.ce
#> 0.3333333