Classification Logistic Regression Learner
mlr_learners_classif.logistic.RdMultinomial Logistic Regression model with a ridge estimator.
Calls RWeka::Logistic() 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 | - | - | |
| C | logical | FALSE | TRUE, FALSE | - |
| R | numeric | - | \((-\infty, \infty)\) | |
| M | integer | -1 | \((-\infty, \infty)\) | |
| 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 | \([1, \infty)\) | |
| options | untyped | NULL | - |
References
le Cessie, S., van Houwelingen, J.C. (1992). “Ridge Estimators in Logistic Regression.” Applied Statistics, 41(1), 191-201.
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 -> LearnerClassifLogistic
Examples
# Define the Learner
learner = mlr3::lrn("classif.logistic")
print(learner)
#> <LearnerClassifLogistic:classif.logistic>: Multinomial 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)
#> Logistic Regression with ridge parameter of 1.0E-8
#> Coefficients...
#> Class
#> Variable M
#> ===================================
#> V1 856.0409
#> V10 100.2502
#> V11 105.2337
#> V12 56.4104
#> V13 -121.8524
#> V14 80.3019
#> V15 -69.6213
#> V16 -38.2395
#> V17 -92.7346
#> V18 42.4048
#> V19 36.3077
#> V2 410.3048
#> V20 -35.9832
#> V21 58.3616
#> V22 106.5182
#> V23 -52.0949
#> V24 -22.3707
#> V25 -14.7983
#> V26 94.3053
#> V27 24.3595
#> V28 -192.7841
#> V29 185.9052
#> V3 -576.9742
#> V30 53.0785
#> V31 -177.4892
#> V32 83.5197
#> V33 18.9019
#> V34 -182.7588
#> V35 122.6429
#> V36 -78.0392
#> V37 5.8686
#> V38 -69.6639
#> V39 -0.9766
#> V4 -125.4748
#> V40 61.802
#> V41 10.8965
#> V42 -102.747
#> V43 211.003
#> V44 -13.9314
#> V45 -79.4387
#> V46 -17.5956
#> V47 343.4199
#> V48 175.06
#> V49 287.178
#> V5 368.8382
#> V50 -2264.8163
#> V51 171.111
#> V52 3549.4015
#> V53 -746.4393
#> V54 367.5736
#> V55 -1869.2159
#> V56 2299.5509
#> V57 -1832.7964
#> V58 -1134.1294
#> V59 3298.438
#> V6 -97.8123
#> V60 121.6509
#> V7 83.1975
#> V8 -377.2979
#> V9 261.4174
#> Intercept -126.7289
#>
#>
#> Odds Ratios...
#> Class
#> Variable M
#> ===================================
#> V1 Infinity
#> V10 3.452366966647695E43
#> V11 5.0400319224602517E45
#> V12 3.1529522850058986E24
#> V13 0
#> V14 7.492989424716826E34
#> V15 0
#> V16 0
#> V17 0
#> V18 2.6070569419691699E18
#> V19 5.864684141094729E15
#> V2 1.5599133015846083E178
#> V20 0
#> V21 2.218769626449977E25
#> V22 1.8208527953856137E46
#> V23 0
#> V24 0
#> V25 0
#> V26 9.042140111945935E40
#> V27 3.794921438961709E10
#> V28 0
#> V29 5.465090114317463E80
#> V3 0
#> V30 1.1263687097466933E23
#> V31 0
#> V32 1.8712953152114344E36
#> V33 161797934.3905
#> V34 0
#> V35 1.832953454082363E53
#> V36 0
#> V37 353.7497
#> V38 0
#> V39 0.3766
#> V4 0
#> V40 6.922390553532315E26
#> V41 53984.5659
#> V42 0
#> V43 4.339691112819297E91
#> V44 0
#> V45 0
#> V46 0
#> V47 1.3975656815521995E149
#> V48 1.0656346751270499E76
#> V49 5.245685892129216E124
#> V5 1.5289127478441933E160
#> V50 0
#> V51 2.0538505381952173E74
#> V52 Infinity
#> V53 0
#> V54 4.316957223260946E159
#> V55 0
#> V56 Infinity
#> V57 0
#> V58 0
#> V59 Infinity
#> V6 0
#> V60 6.79687672216178E52
#> V7 1.3558644244958056E36
#> V8 0
#> V9 3.4051604189735284E113
#>
# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)
# Score the predictions
predictions$score()
#> classif.ce
#> 0.3188406