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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

IdTypeDefaultLevelsRange
subsetuntyped--
na.actionuntyped--
Nlogical-TRUE, FALSE-
Ulogical-TRUE, FALSE-
Rlogical-TRUE, FALSE-
Minteger4\((-\infty, \infty)\)
output_debug_infologicalFALSETRUE, FALSE-
do_not_check_capabilitieslogicalFALSETRUE, FALSE-
num_decimal_placesinteger2\([1, \infty)\)
batch_sizeinteger100\([1, \infty)\)
Llogical-TRUE, FALSE-
optionsuntypedNULL-

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

Author

damirpolat

Super classes

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

Methods

Inherited methods


Method new()

Creates a new instance of this R6 class.

Usage


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)
#> 
#> disp <= 192.5 : LM1 (10/13.261%)
#> disp >  192.5 : 
#> |   drat <= 3.115 : LM2 (5/24.534%)
#> |   drat >  3.115 : LM3 (6/13.467%)
#> 
#> LM num: 1
#> mpg = 
#> 	-2.3799 * am 
#> 	- 0.3978 * carb 
#> 	- 0.0132 * disp 
#> 	- 4.3456 * wt 
#> 	+ 39.496
#> 
#> LM num: 2
#> mpg = 
#> 	-0.686 * carb 
#> 	- 0.0127 * disp 
#> 	- 1.8144 * wt 
#> 	+ 28.9996
#> 
#> LM num: 3
#> mpg = 
#> 	-0.3825 * carb 
#> 	- 0.0127 * disp 
#> 	- 1.7828 * wt 
#> 	+ 28.0824
#> 
#> Number of Rules : 3


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

# Score the predictions
predictions$score()
#> regr.mse 
#> 12.10263