Classification JRip Learner

Repeated Incremental Pruning to Produce Error Reduction. Calls RWeka::JRip() from RWeka.

Dictionary

This Learner can be instantiated via lrn():

lrn("classif.JRip")

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	-		-
F	integer	3		\([2, \infty)\)
N	numeric	2		\([0, \infty)\)
O	integer	2		\([1, \infty)\)
D	logical	FALSE	TRUE, FALSE	-
S	integer	1		\([1, \infty)\)
E	logical	FALSE	TRUE, FALSE	-
P	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		-

Parameter changes

• 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

Cohen, W W (1995). “Fast effective rule induction.” In Machine learning proceedings 1995, 115–123. 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 -> LearnerClassifJRip

Methods

Public methods

• LearnerClassifJRip$new()

• LearnerClassifJRip$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

LearnerClassifJRip$new()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClassifJRip$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Define the Learner
learner = mlr3::lrn("classif.JRip")
print(learner)
#> <LearnerClassifJRip:classif.JRip>: Propositional Rule Learner.
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, mlr3extralearners, RWeka
#> * Predict Types:  [response], prob
#> * Feature Types: integer, numeric, factor, ordered
#> * Properties: 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)
#> JRIP rules:
#> ===========
#> 
#> (V36 >= 0.4546) and (V45 <= 0.2641) => Class=R (37.0/2.0)
#> (V48 <= 0.0744) and (V20 <= 0.5122) => Class=R (15.0/0.0)
#>  => Class=M (87.0/15.0)
#> 
#> Number of Rules : 3
#> 


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()
#> classif.ce 
#>  0.3188406