Regression Decision Table Learner
mlr_learners_regr.decision_table.Rd
Simple Decision Table majority regressor.
Calls RWeka::make_Weka_classifier()
from RWeka.
Initial parameter values
E
:Has only 2 out of 4 original evaluation measures : rmse and mae with rmse being the default
Reason for change: this learner should only contain evaluation measures appropriate for regression tasks
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
P_best
:original id: P
D_best
:original id: D
N_best
:original id: N
S_best
:original id: S
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 | - | - | |
S | character | BestFirst | BestFirst, GreedyStepwise | - |
X | integer | 1 | \((-\infty, \infty)\) | |
E | character | rmse | rmse, mae | - |
I | logical | - | TRUE, FALSE | - |
R | logical | - | TRUE, FALSE | - |
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 | \([1, \infty)\) | |
P_best | untyped | - | - | |
D_best | character | 1 | 0, 1, 2 | - |
N_best | integer | - | \((-\infty, \infty)\) | |
S_best | integer | 1 | \((-\infty, \infty)\) | |
options | untyped | NULL | - |
References
Kohavi R (1995). “The Power of Decision Tables.” In 8th European Conference on Machine Learning, 174--189.
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
-> LearnerRegrDecisionTable
Examples
learner = mlr3::lrn("regr.decision_table")
print(learner)
#> <LearnerRegrDecisionTable:regr.decision_table>: Decision Table
#> * Model: -
#> * Parameters: E=rmse
#> * 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] "S" "X"
#> [5] "E" "I"
#> [7] "R" "output_debug_info"
#> [9] "do_not_check_capabilities" "num_decimal_places"
#> [11] "batch_size" "P_best"
#> [13] "D_best" "N_best"
#> [15] "S_best" "options"