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 -230.2221
#> V10 -135.151
#> V11 48.824
#> V12 417.2917
#> V13 -152.1915
#> V14 182.7414
#> V15 -169.1868
#> V16 68.2488
#> V17 -92.1824
#> V18 45.0013
#> V19 -65.0617
#> V2 1457.7774
#> V20 77.7137
#> V21 -137.3261
#> V22 156.4719
#> V23 57.7409
#> V24 66.5773
#> V25 25.4364
#> V26 -229.8009
#> V27 104.1531
#> V28 6.5144
#> V29 -98.2244
#> V3 -879.1599
#> V30 354.8184
#> V31 -438.4708
#> V32 171.8025
#> V33 84.6273
#> V34 -300.9054
#> V35 250.9485
#> V36 17.2276
#> V37 -245.9505
#> V38 83.1282
#> V39 -25.1224
#> V4 200.4187
#> V40 -1.3483
#> V41 -64.2081
#> V42 -104.7823
#> V43 71.2164
#> V44 39.5982
#> V45 -22.403
#> V46 -30.6689
#> V47 602.3224
#> V48 -20.3071
#> V49 369.1374
#> V5 -175.6671
#> V50 -2120.9226
#> V51 817.6114
#> V52 891.4774
#> V53 2657.2073
#> V54 -451.4863
#> V55 50.0191
#> V56 -1322.0291
#> V57 -409.7399
#> V58 215.111
#> V59 1783.5366
#> V6 241.9771
#> V60 1101.155
#> V7 -265.1094
#> V8 -343.138
#> V9 332.3405
#> Intercept -102.581
#>
#>
#> Odds Ratios...
#> Class
#> Variable M
#> ===================================
#> V1 0
#> V10 0
#> V11 1.5995043727542203E21
#> V12 1.6883624222399055E181
#> V13 0
#> V14 2.3099139338446317E79
#> V15 0
#> V16 4.365833470820818E29
#> V17 0
#> V18 3.498065809738885E19
#> V19 0
#> V2 Infinity
#> V20 5.631750650328867E33
#> V21 0
#> V22 9.013669133223818E67
#> V23 1.1927850060905918E25
#> V24 8.206093358480686E28
#> V25 1.1140144013306358E11
#> V26 0
#> V27 1.7104669322396844E45
#> V28 674.8011
#> V29 0
#> V3 0
#> V30 1.2464098212107336E154
#> V31 0
#> V32 4.1009983204527753E74
#> V33 5.664794378502789E36
#> V34 0
#> V35 9.672783471834669E108
#> V36 30329599.8345
#> V37 0
#> V38 1.2650266291222363E36
#> V39 0
#> V4 1.0983025008485249E87
#> V40 0.2597
#> V41 0
#> V42 0
#> V43 8.490009106706466E30
#> V44 1.57506850820602656E17
#> V45 0
#> V46 0
#> V47 3.8486874692045793E261
#> V48 0
#> V49 2.0623265929459407E160
#> V5 0
#> V50 0
#> V51 Infinity
#> V52 Infinity
#> V53 Infinity
#> V54 0
#> V55 5.28462809468674E21
#> V56 0
#> V57 0
#> V58 2.6395170780400906E93
#> V59 Infinity
#> V6 1.2283710795012565E105
#> V60 Infinity
#> V7 0
#> V8 0
#> V9 2.1559485542623307E144
#>
# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)
# Score the predictions
predictions$score()
#> classif.ce
#> 0.2318841