Classification SimpleLogistic Learner
mlr_learners_classif.simple_logistic.Rd
LogitBoost with simple regression functions as base learners.
Calls RWeka::make_Weka_classifier()
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 | - | - | |
I | integer | - | \((-\infty, \infty)\) | |
S | logical | FALSE | TRUE, FALSE | - |
P | logical | FALSE | TRUE, FALSE | - |
M | integer | - | \((-\infty, \infty)\) | |
H | integer | 50 | \((-\infty, \infty)\) | |
W | numeric | 0 | \((-\infty, \infty)\) | |
A | logical | FALSE | 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 | \([1, \infty)\) | |
options | untyped | NULL | - |
References
Landwehr, Niels, Hall, Mark, Frank, Eibe (2005). “Logistic model trees.” Machine learning, 59(1), 161--205.
Sumner M, Frank E, Hall M (2005). “Speeding up Logistic Model Tree Induction.” In 9th European Conference on Principles and Practice of Knowledge Discovery in Databases, 675-683.
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::LearnerClassif
-> LearnerClassifSimpleLogistic
Examples
learner = mlr3::lrn("classif.simple_logistic")
print(learner)
#> <LearnerClassifSimpleLogistic:classif.simple_logistic>: LogitBoost Based Logistic Regression
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, RWeka
#> * Predict Types: [response], prob
#> * Feature Types: logical, integer, numeric, factor, ordered
#> * Properties: missings, multiclass, twoclass
# available parameters:
learner$param_set$ids()
#> [1] "subset" "na.action"
#> [3] "I" "S"
#> [5] "P" "M"
#> [7] "H" "W"
#> [9] "A" "output_debug_info"
#> [11] "do_not_check_capabilities" "num_decimal_places"
#> [13] "batch_size" "options"