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
Dictionary
This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn()
:
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
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: -
# available parameters:
learner$param_set$ids()
#> [1] "subset" "na.action"
#> [3] "N" "U"
#> [5] "R" "M"
#> [7] "output_debug_info" "do_not_check_capabilities"
#> [9] "num_decimal_places" "batch_size"
#> [11] "L" "options"