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 727.3241
#> V10 -205.0089
#> V11 287.3926
#> V12 -125.9558
#> V13 190.7647
#> V14 -32.7239
#> V15 -19.2913
#> V16 -83.5939
#> V17 -66.8009
#> V18 108.7629
#> V19 43.5547
#> V2 960.3945
#> V20 -105.4852
#> V21 35.2961
#> V22 9.6406
#> V23 54.0488
#> V24 137.1211
#> V25 -198.6339
#> V26 -42.7922
#> V27 98.8662
#> V28 40.3219
#> V29 -62.6193
#> V3 -1703.7003
#> V30 76.7544
#> V31 -312.3554
#> V32 227.1296
#> V33 -60.3367
#> V34 57.0698
#> V35 43.2203
#> V36 -200.8332
#> V37 84.3662
#> V38 65.2132
#> V39 -44.6404
#> V4 531.1252
#> V40 -226.6617
#> V41 210.7182
#> V42 -29.9269
#> V43 262.0964
#> V44 -226.8734
#> V45 -35.2664
#> V46 153.8406
#> V47 361.7336
#> V48 -185.5599
#> V49 865.2278
#> V5 -251.5659
#> V50 -2607.2632
#> V51 878.7165
#> V52 1889.4755
#> V53 324.8998
#> V54 81.2787
#> V55 -279.1898
#> V56 940.819
#> V57 1978.0214
#> V58 -589.2651
#> V59 -155.9757
#> V6 238.1316
#> V60 -672.4713
#> V7 -171.035
#> V8 -211.1843
#> V9 227.4977
#> Intercept -67.2787
#>
#>
#> Odds Ratios...
#> Class
#> Variable M
#> ===================================
#> V1 Infinity
#> V10 0
#> V11 6.50154573643597E124
#> V12 0
#> V13 7.047582555441164E82
#> V14 0
#> V15 0
#> V16 0
#> V17 0
#> V18 1.7184175896978863E47
#> V19 8.2331543531672074E18
#> V2 Infinity
#> V20 0
#> V21 2.132504136444066E15
#> V22 15376.7148
#> V23 2.9722099233608443E23
#> V24 3.5558170540946657E59
#> V25 0
#> V26 0
#> V27 8.650704075271349E42
#> V28 3.2478779834819859E17
#> V29 0
#> V3 0
#> V30 2.1577957394348164E33
#> V31 0
#> V32 4.3767065328819796E98
#> V33 0
#> V34 6.096655533479562E24
#> V35 5.8932417129522166E18
#> V36 0
#> V37 4.3627370021969334E36
#> V38 2.0975898550055196E28
#> V39 0
#> V4 4.62124629133135E230
#> V40 0
#> V41 3.263939493052663E91
#> V42 0
#> V43 6.71492082382858E113
#> V44 0
#> V45 0
#> V46 6.4880229989774545E66
#> V47 1.255746612710446E157
#> V48 0
#> V49 Infinity
#> V5 0
#> V50 0
#> V51 Infinity
#> V52 Infinity
#> V53 1.2652433213935182E141
#> V54 1.9900914190378953E35
#> V55 0
#> V56 Infinity
#> V57 Infinity
#> V58 0
#> V59 0
#> V6 2.6255508369794266E103
#> V60 0
#> V7 0
#> V8 0
#> V9 6.324228724744786E98
#>
# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)
# Score the predictions
predictions$score()
#> classif.ce
#> 0.3188406