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 Lo
#> • Model: -
#> • Parameters: list()
#> • Packages: mlr3 and RWeka
#> • Predict Types: [response] and prob
#> • Feature Types: logical, integer, numeric, factor, and ordered
#> • Encapsulation: none (fallback: -)
#> • Properties: missings, multiclass, and twoclass
#> • Other settings: use_weights = 'error'
# 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$model)
#> SimpleLogistic:
#>
#> Class M :
#> -4.88 +
#> [V1] * 9.75 +
#> [V11] * 6.18 +
#> [V12] * 2.16 +
#> [V16] * -1.47 +
#> [V17] * -0.74 +
#> [V21] * 0.89 +
#> [V23] * 1.54 +
#> [V24] * 0.98 +
#> [V28] * 0.39 +
#> [V3] * -6.78 +
#> [V31] * -0.51 +
#> [V34] * -0.69 +
#> [V35] * 0.4 +
#> [V36] * -1.46 +
#> [V37] * -2.11 +
#> [V38] * 1.5 +
#> [V39] * 0.61 +
#> [V4] * 6.1 +
#> [V40] * -0.61 +
#> [V42] * -1.06 +
#> [V43] * 3.54 +
#> [V45] * 3.81 +
#> [V48] * 3.86 +
#> [V49] * 7.33 +
#> [V5] * 2.06 +
#> [V50] * -14.73 +
#> [V52] * 10.28 +
#> [V53] * 39.23 +
#> [V54] * 36.38 +
#> [V59] * 47.8 +
#> [V6] * -1.85 +
#> [V7] * -1.57 +
#> [V8] * -6.13 +
#> [V9] * 2.98
#>
#> Class R :
#> 4.88 +
#> [V1] * -9.75 +
#> [V11] * -6.18 +
#> [V12] * -2.16 +
#> [V16] * 1.47 +
#> [V17] * 0.74 +
#> [V21] * -0.89 +
#> [V23] * -1.54 +
#> [V24] * -0.98 +
#> [V28] * -0.39 +
#> [V3] * 6.78 +
#> [V31] * 0.51 +
#> [V34] * 0.69 +
#> [V35] * -0.4 +
#> [V36] * 1.46 +
#> [V37] * 2.11 +
#> [V38] * -1.5 +
#> [V39] * -0.61 +
#> [V4] * -6.1 +
#> [V40] * 0.61 +
#> [V42] * 1.06 +
#> [V43] * -3.54 +
#> [V45] * -3.81 +
#> [V48] * -3.86 +
#> [V49] * -7.33 +
#> [V5] * -2.06 +
#> [V50] * 14.73 +
#> [V52] * -10.28 +
#> [V53] * -39.23 +
#> [V54] * -36.38 +
#> [V59] * -47.8 +
#> [V6] * 1.85 +
#> [V7] * 1.57 +
#> [V8] * 6.13 +
#> [V9] * -2.98
#>
# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)
# Score the predictions
predictions$score()
#> classif.ce
#> 0.2173913