Regression M5P Learner

Implements base routines for generating M5 Model trees and rules. Calls RWeka::M5P() 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

Dictionary

This Learner can be instantiated via lrn():

lrn("regr.m5p")

Meta Information

• Task type: “regr”

• Predict Types: “response”

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

• Required Packages: mlr3, RWeka

Parameters

Id	Type	Default	Levels	Range
subset	untyped	-		-
na.action	untyped	-		-
N	logical	-	TRUE, FALSE	-
U	logical	-	TRUE, FALSE	-
R	logical	-	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		\([1, \infty)\)
L	logical	-	TRUE, FALSE	-
options	untyped	NULL		-

References

Quinlan RJ (1992). “Learning with Continuous Classes.” In 5th Australian Joint Conference on Artificial Intelligence, 343-348.

Wang Y, Witten IH (1997). “Induction of model trees for predicting continuous classes.” In Poster papers of the 9th European Conference on Machine Learning.

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

damirpolat

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrM5P

Methods

Public methods

• LearnerRegrM5P$new()

• LearnerRegrM5P$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

LearnerRegrM5P$new()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerRegrM5P$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Define the Learner
learner = mlr3::lrn("regr.m5p")
print(learner)
#> <LearnerRegrM5P:regr.m5p>: M5P
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, RWeka
#> * Predict Types:  [response]
#> * Feature Types: logical, 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 tree:
#> (using smoothed linear models)
#> 
#> wt <= 2.26 : LM1 (5/41.924%)
#> wt >  2.26 : LM2 (16/24.402%)
#> 
#> LM num: 1
#> mpg = 
#> 	-0.0309 * hp 
#> 	- 3.2555 * wt 
#> 	+ 37.4557
#> 
#> LM num: 2
#> mpg = 
#> 	-0.0369 * hp 
#> 	- 3.4971 * wt 
#> 	+ 35.8751
#> 
#> Number of Rules : 2


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

# Score the predictions
predictions$score()
#> regr.mse 
#>  5.96283