Regression M5Rules Learner

Algorithm for inducing decision lists from model trees. Calls RWeka::M5Rules() from RWeka.

Dictionary

This Learner can be instantiated via lrn():

lrn("regr.M5Rules")

Meta Information

• Task type: “regr”

• Predict Types: “response”

• Feature Types: “integer”, “numeric”, “factor”, “ordered”

• Required Packages: mlr3, mlr3extralearners, RWeka

Parameters

Id	Type	Default	Levels	Range
subset	untyped	-		-
na.action	untyped	-		-
N	logical	FALSE	TRUE, FALSE	-
U	logical	FALSE	TRUE, FALSE	-
R	logical	FALSE	TRUE, FALSE	-
M	integer	4		\((-\infty, \infty)\)
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

Holmes, Geoffrey, Hall, Mark, Prank, Eibe (1999). “Generating rule sets from model trees.” In Australasian joint conference on artificial intelligence, 1–12. Springer.

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::LearnerRegr -> LearnerRegrM5Rules

Methods

Public methods

• LearnerRegrM5Rules$new()

• LearnerRegrM5Rules$clone()

Inherited methods

• mlr3::Learner$base_learner()
• mlr3::Learner$encapsulate()
• 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

LearnerRegrM5Rules$new()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerRegrM5Rules$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Define the Learner
learner = mlr3::lrn("regr.M5Rules")
print(learner)
#> <LearnerRegrM5Rules:regr.M5Rules>: Rule-based Algorithm
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, mlr3extralearners, RWeka
#> * Predict Types:  [response]
#> * Feature Types: integer, numeric, factor, ordered
#> * Properties: -

# Define a Task
task = mlr3::tsk("mtcars")

# 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)
#> M5 pruned model rules 
#> (using smoothed linear models) :
#> Number of Rules : 3
#> 
#> Rule: 1
#> IF
#> 	cyl > 5
#> THEN
#> 
#> mpg = 
#> 	2.6721 * am 
#> 	- 0.0264 * hp 
#> 	+ 0.3016 * qsec 
#> 	- 2.1469 * wt 
#> 	+ 23.9103 [12/21.375%]
#> 
#> Rule: 2
#> IF
#> 	disp > 101.55
#> THEN
#> 
#> mpg = 
#> 	-0.1053 * disp 
#> 	+ 37.4502 [5/35.404%]
#> 
#> Rule: 3
#> 
#> mpg = 
#> 	1.0845 * qsec 
#> 	+ 11.4969 [4/53.076%]
#> 
#> 


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

# Score the predictions
predictions$score()
#> regr.mse 
#> 5.707644