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 733.5445
#> V10 195.232
#> V11 -364.0916
#> V12 390.4501
#> V13 -35.2359
#> V14 -72.0029
#> V15 33.4353
#> V16 -137.2323
#> V17 30.7406
#> V18 41.3565
#> V19 -39.1134
#> V2 893.5245
#> V20 170.4279
#> V21 -189.8346
#> V22 11.7356
#> V23 139.5471
#> V24 11.4528
#> V25 10.2996
#> V26 -114.7194
#> V27 254.679
#> V28 -320.3922
#> V29 79.3297
#> V3 -1942.0212
#> V30 318.7298
#> V31 -457.7951
#> V32 158.2991
#> V33 38.4505
#> V34 -206.4537
#> V35 239.3246
#> V36 -58.5228
#> V37 -283.5473
#> V38 120.8432
#> V39 57.7903
#> V4 1156.7887
#> V40 -49.7414
#> V41 41.4745
#> V42 -162.7426
#> V43 304.6831
#> V44 -270.2434
#> V45 121.163
#> V46 58.0014
#> V47 270.6726
#> V48 704.6193
#> V49 627.9559
#> V5 -333.9649
#> V50 -4823.6422
#> V51 -13.2875
#> V52 1873.113
#> V53 2621.2681
#> V54 1051.0112
#> V55 -46.4369
#> V56 -1054.498
#> V57 1570.7881
#> V58 1242.2772
#> V59 1400.8704
#> V6 122.4493
#> V60 -1557.7076
#> V7 -191.2181
#> V8 -493.9877
#> V9 424.6157
#> Intercept -108.7556
#>
#>
#> Odds Ratios...
#> Class
#> Variable M
#> ===================================
#> V1 Infinity
#> V10 6.140336069602938E84
#> V11 0
#> V12 3.718166273282378E169
#> V13 0
#> V14 0
#> V15 3.31703224473088E14
#> V16 0
#> V17 2.2411177022159086E13
#> V18 9.1387309052609254E17
#> V19 0
#> V2 Infinity
#> V20 1.0372917197805367E74
#> V21 0
#> V22 124944.6764
#> V23 4.0230474256105404E60
#> V24 94164.9364
#> V25 29720.0404
#> V26 0
#> V27 4.0334463460270255E110
#> V28 0
#> V29 2.8342189878903416E34
#> V3 0
#> V30 2.6460087024209776E138
#> V31 0
#> V32 5.602950563511022E68
#> V33 4.9983402122977712E16
#> V34 0
#> V35 8.656322083388165E103
#> V36 0
#> V37 0
#> V38 3.0307030239318224E52
#> V39 1.2531652365954075E25
#> V4 Infinity
#> V40 0
#> V41 1.02835755296345114E18
#> V42 0
#> V43 2.099833344853066E132
#> V44 0
#> V45 4.1727821995001807E52
#> V46 1.5477549609542595E25
#> V47 3.561283342482355E117
#> V48 1.0286681621613908E306
#> V49 5.221340756994553E272
#> V5 0
#> V50 0
#> V51 0
#> V52 Infinity
#> V53 Infinity
#> V54 Infinity
#> V55 0
#> V56 0
#> V57 Infinity
#> V58 Infinity
#> V59 Infinity
#> V6 1.5103394615225896E53
#> V60 0
#> V7 0
#> V8 0
#> V9 2.560074669168945E184
#>
# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)
# Score the predictions
predictions$score()
#> classif.ce
#> 0.2318841