Regression M5P Learner
mlr_learners_regr.m5p.Rd
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
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
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.
Super classes
mlr3::Learner
-> mlr3::LearnerRegr
-> LearnerRegrM5P
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