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 972.3861
#> V10 290.8686
#> V11 -95.3154
#> V12 102.5803
#> V13 263.9428
#> V14 -240.7248
#> V15 229.9665
#> V16 -154.5266
#> V17 -289.5627
#> V18 360.923
#> V19 -219.4057
#> V2 -421.8133
#> V20 74.6116
#> V21 2.3785
#> V22 101.2254
#> V23 4.7451
#> V24 131.0988
#> V25 -30.1107
#> V26 -129.719
#> V27 104.5638
#> V28 -82.5915
#> V29 -31.745
#> V3 -230.1082
#> V30 253.0465
#> V31 -229.719
#> V32 141.1334
#> V33 -30.9638
#> V34 -1.8354
#> V35 -4.7523
#> V36 -53.6382
#> V37 88.1524
#> V38 -138.0823
#> V39 191.1364
#> V4 -120.3186
#> V40 -198.8482
#> V41 41.5236
#> V42 11.7203
#> V43 -98.2833
#> V44 -25.3354
#> V45 190.9621
#> V46 -93.612
#> V47 340.4143
#> V48 -849.1848
#> V49 2530.7623
#> V5 345.6242
#> V50 -3880.4774
#> V51 2953.8419
#> V52 208.7079
#> V53 3050.5521
#> V54 409.7566
#> V55 -222.2284
#> V56 3242.8867
#> V57 -3007.8884
#> V58 -576.6471
#> V59 -2086.1146
#> V6 2.2864
#> V60 117.1887
#> V7 14.1527
#> V8 -303.784
#> V9 5.6449
#> Intercept -205.3723
#>
#>
#> Odds Ratios...
#> Class
#> Variable M
#> ===================================
#> V1 Infinity
#> V10 2.1019185069640957E126
#> V11 0
#> V12 3.5484851016804918E44
#> V13 4.254948080658715E114
#> V14 0
#> V15 7.467453866369425E99
#> V16 0
#> V17 0
#> V18 5.583165644135464E156
#> V19 0
#> V2 0
#> V20 2.5317364364583453E32
#> V21 10.7883
#> V22 9.154861878437914E43
#> V23 115.0167
#> V24 8.619453284556147E56
#> V25 0
#> V26 0
#> V27 2.5792673427725075E45
#> V28 0
#> V29 0
#> V3 0
#> V30 7.882980672076462E109
#> V31 0
#> V32 1.9653521601549125E61
#> V33 0
#> V34 0.1596
#> V35 0.0086
#> V36 0
#> V37 1.923555615421264E38
#> V38 0
#> V39 1.0220670304946964E83
#> V4 0
#> V40 0
#> V41 1.08010565203866419E18
#> V42 123044.0927
#> V43 0
#> V44 0
#> V45 8.586185113878775E82
#> V46 0
#> V47 6.919334143523433E147
#> V48 0
#> V49 Infinity
#> V5 1.2667058443792323E150
#> V50 0
#> V51 Infinity
#> V52 4.37210182496507E90
#> V53 Infinity
#> V54 9.016130464311093E177
#> V55 0
#> V56 Infinity
#> V57 0
#> V58 0
#> V59 0
#> V6 9.8394
#> V60 7.841234848292064E50
#> V7 1400997.9537
#> V8 0
#> V9 282.8375
#>
# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)
# Score the predictions
predictions$score()
#> classif.ce
#> 0.2028986