Classification J48 Learner
mlr_learners_classif.J48.Rd
Decision tree algorithm.
Calls RWeka::IBk()
from RWeka.
Meta Information
Task type: “classif”
Predict Types: “response”, “prob”
Feature Types: “integer”, “numeric”, “factor”, “ordered”
Required Packages: mlr3, mlr3extralearners, RWeka
Parameters
Id | Type | Default | Levels | Range |
subset | untyped | - | - | |
na.action | untyped | - | - | |
U | logical | FALSE | TRUE, FALSE | - |
O | logical | FALSE | TRUE, FALSE | - |
C | numeric | 0.25 | \([2.22044604925031e-16, 1]\) | |
M | integer | 2 | \([1, \infty)\) | |
R | logical | FALSE | TRUE, FALSE | - |
N | integer | 3 | \([2, \infty)\) | |
B | logical | FALSE | TRUE, FALSE | - |
S | logical | FALSE | TRUE, FALSE | - |
L | logical | FALSE | TRUE, FALSE | - |
A | logical | FALSE | TRUE, FALSE | - |
J | logical | FALSE | TRUE, FALSE | - |
Q | integer | 1 | \([1, \infty)\) | |
doNotMakeSplitPointActualValue | 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 | - |
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
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
-> LearnerClassifJ48
Examples
# Define the Learner
learner = mlr3::lrn("classif.J48")
print(learner)
#> <LearnerClassifJ48:classif.J48>: Tree-based Model
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, mlr3extralearners, RWeka
#> * Predict Types: [response], prob
#> * Feature Types: 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$model)
#> J48 pruned tree
#> ------------------
#>
#> V11 <= 0.197
#> | V59 <= 0.0093
#> | | V47 <= 0.1716: R (47.0/2.0)
#> | | V47 > 0.1716: M (3.0)
#> | V59 > 0.0093
#> | | V1 <= 0.0152: R (2.0)
#> | | V1 > 0.0152: M (9.0)
#> V11 > 0.197
#> | V51 <= 0.0128
#> | | V16 <= 0.657
#> | | | V3 <= 0.0577
#> | | | | V5 <= 0.0421
#> | | | | | V12 <= 0.3161: M (2.0)
#> | | | | | V12 > 0.3161: R (2.0)
#> | | | | V5 > 0.0421: M (13.0)
#> | | | V3 > 0.0577: R (3.0)
#> | | V16 > 0.657: R (9.0)
#> | V51 > 0.0128
#> | | V34 <= 0.8681: M (44.0/1.0)
#> | | V34 > 0.8681
#> | | | V11 <= 0.2509: R (3.0)
#> | | | V11 > 0.2509: M (2.0)
#>
#> Number of Leaves : 12
#>
#> Size of the tree : 23
#>
# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)
# Score the predictions
predictions$score()
#> classif.ce
#> 0.3043478