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
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
# Define the Learner
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
# Define a Task
task = mlr3::tsk("sonar")
# 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$model)
#> SimpleLogistic:
#>
#> Class M :
#> -1.86 +
#> [V1] * 11.54 +
#> [V11] * 3.28 +
#> [V12] * 0.93 +
#> [V16] * -1.31 +
#> [V20] * 0.72 +
#> [V21] * 0.5 +
#> [V27] * 0.49 +
#> [V29] * 0.74 +
#> [V31] * -1.78 +
#> [V36] * -1.65 +
#> [V37] * -0.52 +
#> [V44] * 3.13 +
#> [V45] * 1.38 +
#> [V48] * 2.2 +
#> [V49] * 8.17 +
#> [V50] * -20.08 +
#> [V52] * 40.96 +
#> [V57] * -44.35 +
#> [V58] * 56.39 +
#> [V59] * 46.8 +
#> [V7] * -2.86 +
#> [V8] * -1.69
#>
#> Class R :
#> 1.86 +
#> [V1] * -11.54 +
#> [V11] * -3.28 +
#> [V12] * -0.93 +
#> [V16] * 1.31 +
#> [V20] * -0.72 +
#> [V21] * -0.5 +
#> [V27] * -0.49 +
#> [V29] * -0.74 +
#> [V31] * 1.78 +
#> [V36] * 1.65 +
#> [V37] * 0.52 +
#> [V44] * -3.13 +
#> [V45] * -1.38 +
#> [V48] * -2.2 +
#> [V49] * -8.17 +
#> [V50] * 20.08 +
#> [V52] * -40.96 +
#> [V57] * 44.35 +
#> [V58] * -56.39 +
#> [V59] * -46.8 +
#> [V7] * 2.86 +
#> [V8] * 1.69
#>
# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)
# Score the predictions
predictions$score()
#> classif.ce
#> 0.2028986