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 126.6623
#> V10 16.4023
#> V11 -25.6143
#> V12 255.1191
#> V13 -59.8847
#> V14 -44.6748
#> V15 155.8188
#> V16 -147.8675
#> V17 -39.4575
#> V18 69.0097
#> V19 -22.2105
#> V2 297.7799
#> V20 124.0989
#> V21 13.314
#> V22 -89.1604
#> V23 -82.6749
#> V24 235.5432
#> V25 -3.7101
#> V26 -176.012
#> V27 122.9785
#> V28 -34.5803
#> V29 -70.8677
#> V3 -1266.1626
#> V30 169.9365
#> V31 -183.9388
#> V32 77.1596
#> V33 14.2359
#> V34 -186.0841
#> V35 232.0078
#> V36 -85.5062
#> V37 -157.258
#> V38 96.8776
#> V39 6.3471
#> V4 526.8573
#> V40 -34.7045
#> V41 -48.3228
#> V42 94.9773
#> V43 -144.529
#> V44 156.4525
#> V45 37.536
#> V46 84.3824
#> V47 151.7934
#> V48 -130.1998
#> V49 811.7201
#> V5 -74.8193
#> V50 -912.1905
#> V51 -962.0994
#> V52 676.2085
#> V53 2214.2212
#> V54 1573.4411
#> V55 -2539.9879
#> V56 -1617.2445
#> V57 -87.7868
#> V58 107.2792
#> V59 1912.5582
#> V6 250.4293
#> V60 -963.335
#> V7 -624.0902
#> V8 -161.3361
#> V9 265.6141
#> Intercept -56.6901
#>
#>
#> Odds Ratios...
#> Class
#> Variable M
#> ===================================
#> V1 1.0203581867611569E55
#> V10 13287228.0996
#> V11 0
#> V12 6.263578810260428E110
#> V13 0
#> V14 0
#> V15 4.6906518866336226E67
#> V16 0
#> V17 0
#> V18 9.344363338221749E29
#> V19 0
#> V2 2.1093959033123522E129
#> V20 7.860470500855482E53
#> V21 605637.1107
#> V22 0
#> V23 0
#> V24 1.9730267287681956E102
#> V25 0.0245
#> V26 0
#> V27 2.5638344983929824E53
#> V28 0
#> V29 0
#> V3 0
#> V30 6.345827116536759E73
#> V31 0
#> V32 3.235921106937759E33
#> V33 1522506.2455
#> V34 0
#> V35 5.7503765995980766E100
#> V36 0
#> V37 0
#> V38 1.1841102746078983E42
#> V39 570.8376
#> V4 6.474467644874807E228
#> V40 0
#> V41 0
#> V42 1.770609285712916E41
#> V43 0
#> V44 8.840239240906072E67
#> V45 2.0029006323039576E16
#> V46 4.4343661762691566E36
#> V47 8.376401502867353E65
#> V48 0
#> V49 Infinity
#> V5 0
#> V50 0
#> V51 0
#> V52 4.7166850291763065E293
#> V53 Infinity
#> V54 Infinity
#> V55 0
#> V56 0
#> V57 0
#> V58 3.8974508639124203E46
#> V59 Infinity
#> V6 5.754993891974163E108
#> V60 0
#> V7 0
#> V8 0
#> V9 2.263256348549002E115
#>
# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)
# Score the predictions
predictions$score()
#> classif.ce
#> 0.3768116