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 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)
#> Logistic Regression with ridge parameter of 1.0E-8
#> Coefficients...
#> Class
#> Variable M
#> ===================================
#> V1 234.4741
#> V10 -168.3497
#> V11 95.8339
#> V12 126.0161
#> V13 120.0834
#> V14 -83.7475
#> V15 2.3399
#> V16 -34.2557
#> V17 9.0959
#> V18 -26.8416
#> V19 44.8569
#> V2 505.0166
#> V20 -26.6068
#> V21 -2.3816
#> V22 -24.9652
#> V23 0.1337
#> V24 159.0497
#> V25 -56.3656
#> V26 -114.2229
#> V27 32.6665
#> V28 -14.9416
#> V29 -37.7311
#> V3 -531.5841
#> V30 158.2676
#> V31 -216.4763
#> V32 33.5987
#> V33 55.1337
#> V34 -102.8862
#> V35 128.8825
#> V36 -156.5877
#> V37 -13.3755
#> V38 -58.7303
#> V39 103.0508
#> V4 21.0206
#> V40 -39.5133
#> V41 -35.7022
#> V42 35.5515
#> V43 14.5269
#> V44 54.3705
#> V45 76.358
#> V46 117.6125
#> V47 -188.6731
#> V48 611.2193
#> V49 -357.2244
#> V5 -20.7021
#> V50 -165.6328
#> V51 -660.0381
#> V52 897.838
#> V53 593.1662
#> V54 -204.7508
#> V55 -4102.361
#> V56 884.8135
#> V57 -1544.5834
#> V58 1929.5003
#> V59 2081.3718
#> V6 11.0069
#> V60 -2984.4924
#> V7 -233.1781
#> V8 -22.0477
#> V9 136.8542
#> Intercept 36.4046
#>
#>
#> Odds Ratios...
#> Class
#> Variable M
#> ===================================
#> V1 6.773093712238356E101
#> V10 0
#> V11 4.17012235709738E41
#> V12 5.3468885122110196E54
#> V13 1.4175625944937342E52
#> V14 0
#> V15 10.3803
#> V16 0
#> V17 8918.7941
#> V18 0
#> V19 3.027699100984806E19
#> V2 2.1180576930437113E219
#> V20 0
#> V21 0.0924
#> V22 0
#> V23 1.143
#> V24 1.1868865288744851E69
#> V25 0
#> V26 0
#> V27 1.5377828922006897E14
#> V28 0
#> V29 0
#> V3 0
#> V30 5.429446741361161E68
#> V31 0
#> V32 3.9059212082766444E14
#> V33 8.795167006010712E23
#> V34 0
#> V35 9.39674958563154E55
#> V36 0
#> V37 0
#> V38 0
#> V39 5.6808778217904796E44
#> V4 1346227752.3527
#> V40 0
#> V41 0
#> V42 2.7529688953268175E15
#> V43 2036719.8113
#> V44 4.100250323271359E23
#> V45 1.4516471825283777E33
#> V46 1.1979722170529673E51
#> V47 0
#> V48 2.8129215445311952E265
#> V49 0
#> V5 0
#> V50 0
#> V51 0
#> V52 Infinity
#> V53 4.0627010249816973E257
#> V54 0
#> V55 0
#> V56 Infinity
#> V57 0
#> V58 Infinity
#> V59 Infinity
#> V6 60287.1823
#> V60 0
#> V7 0
#> V8 0
#> V9 2.7228968820057472E59
#>
# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)
# Score the predictions
predictions$score()
#> classif.ce
#> 0.3188406