Classification J48 Learner

Decision tree algorithm. Calls RWeka::IBk() from RWeka.

Dictionary

This Learner can be instantiated via lrn():

lrn("classif.J48")

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

References

Quinlan, Ross J (2014). C4. 5: programs for machine learning. Elsevier.

See also

• Dictionary of Learners: mlr3::mlr_learners.

• 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.

Author

henrifnk

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifJ48

Methods

Public methods

• LearnerClassifJ48$new()

• LearnerClassifJ48$clone()

Inherited methods

• mlr3::Learner$base_learner()
• mlr3::Learner$format()
• mlr3::Learner$help()
• mlr3::Learner$predict()
• mlr3::Learner$predict_newdata()
• mlr3::Learner$print()
• mlr3::Learner$reset()
• mlr3::Learner$train()

---

Method new()

Creates a new instance of this R6 class.

Usage

LearnerClassifJ48$new()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClassifJ48$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

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