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 963.2409
#> V10 180.949
#> V11 100.2896
#> V12 36.5432
#> V13 -43.3207
#> V14 257.6831
#> V15 -113.1665
#> V16 -47.2853
#> V17 -89.839
#> V18 6.7985
#> V19 101.7469
#> V2 371.3584
#> V20 10.328
#> V21 -32.7191
#> V22 79.1803
#> V23 -32.0114
#> V24 71.7556
#> V25 20.4777
#> V26 -48.7538
#> V27 -17.2345
#> V28 -23.1968
#> V29 3.6524
#> V3 -737.8031
#> V30 296.5409
#> V31 -334.2215
#> V32 284.8906
#> V33 -98.1006
#> V34 -44.9557
#> V35 57.0081
#> V36 -178.9856
#> V37 185.0774
#> V38 -111.6702
#> V39 76.328
#> V4 104.1988
#> V40 -254.9798
#> V41 117.9697
#> V42 130.3248
#> V43 100.2415
#> V44 -62.6831
#> V45 -1.9495
#> V46 110.5788
#> V47 -118.0625
#> V48 -399.7462
#> V49 892.2377
#> V5 240.4746
#> V50 -1352.1521
#> V51 2634.7568
#> V52 719.506
#> V53 -767.2427
#> V54 987.6634
#> V55 -1483.3753
#> V56 1827.9333
#> V57 -2260.1346
#> V58 -967.6498
#> V59 881.6058
#> V6 -69.335
#> V60 1061.9431
#> V7 -124.656
#> V8 -256.1079
#> V9 52.9498
#> Intercept -195.6887
#>
#>
#> Odds Ratios...
#> Class
#> Variable M
#> ===================================
#> V1 Infinity
#> V10 3.847370317640745E78
#> V11 3.591180380986417E43
#> V12 7.421550430339538E15
#> V13 0
#> V14 8.135133927081057E111
#> V15 0
#> V16 0
#> V17 0
#> V18 896.5423
#> V19 1.542152677147186E44
#> V2 1.9007202303576837E161
#> V20 30576.9837
#> V21 0
#> V22 2.4410443582799312E34
#> V23 0
#> V24 1.4556310844914721E31
#> V25 782291815.0178
#> V26 0
#> V27 0
#> V28 0
#> V29 38.5676
#> V3 0
#> V30 6.110727894539722E128
#> V31 0
#> V32 5.326166986603397E123
#> V33 0
#> V34 0
#> V35 5.731808943493481E24
#> V36 0
#> V37 2.3884286934089553E80
#> V38 0
#> V39 1.4087370184379145E33
#> V4 1.790385661245591E45
#> V40 0
#> V41 1.7123834034487543E51
#> V42 3.9750066690859495E56
#> V43 3.4222440846750425E43
#> V44 0
#> V45 0.1424
#> V46 1.0562476720310476E48
#> V47 0
#> V48 0
#> V49 Infinity
#> V5 2.7340552569828604E104
#> V50 0
#> V51 Infinity
#> V52 Infinity
#> V53 0
#> V54 Infinity
#> V55 0
#> V56 Infinity
#> V57 0
#> V58 0
#> V59 Infinity
#> V6 0
#> V60 Infinity
#> V7 0
#> V8 0
#> V9 9.903733459884594E22
#>
# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)
# Score the predictions
predictions$score()
#> classif.ce
#> 0.2898551