Classification Logistic Regression Learner
Source:R/learner_RWeka_classif_logistic.R
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
Methods
Inherited methods
mlr3::Learner$base_learner()mlr3::Learner$configure()mlr3::Learner$encapsulate()mlr3::Learner$format()mlr3::Learner$help()mlr3::Learner$predict()mlr3::Learner$predict_newdata()mlr3::Learner$print()mlr3::Learner$reset()mlr3::Learner$selected_features()mlr3::Learner$train()mlr3::LearnerClassif$predict_newdata_fast()
Method marshal()
Marshal the learner's model.
Arguments
...(any)
Additional arguments passed tomlr3::marshal_model().
Method unmarshal()
Unmarshal the learner's model.
Arguments
...(any)
Additional arguments passed tomlr3::unmarshal_model().
Examples
# Define the Learner
learner = 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: marshal, missings, multiclass, and twoclass
#> • Other settings: use_weights = 'error'
# Define a Task
task = tsk("sonar")
# Create train and test set
ids = 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 901.1824
#> V10 -162.995
#> V11 83.5096
#> V12 166.9887
#> V13 -124.4179
#> V14 71.2766
#> V15 -94.0409
#> V16 -55.0332
#> V17 -7.9525
#> V18 -33.0941
#> V19 168.7483
#> V2 -550.9161
#> V20 -16.2135
#> V21 -160.8317
#> V22 110.9198
#> V23 34.3077
#> V24 80.1052
#> V25 -86.281
#> V26 24.3204
#> V27 87.6375
#> V28 -37.8438
#> V29 -93.333
#> V3 115.4549
#> V30 174.4364
#> V31 -359.7523
#> V32 193.6794
#> V33 98.5796
#> V34 -70.5737
#> V35 -21.5034
#> V36 61.0176
#> V37 -267.9315
#> V38 125.8442
#> V39 135.4389
#> V4 408.2966
#> V40 -99.168
#> V41 -23.875
#> V42 -21.8435
#> V43 195.1498
#> V44 -315.9099
#> V45 90.4076
#> V46 202.0263
#> V47 228.7805
#> V48 11.8106
#> V49 945.8084
#> V5 177.2177
#> V50 -3435.6314
#> V51 2000.2659
#> V52 1214.6149
#> V53 128.9777
#> V54 125.1478
#> V55 -657.4418
#> V56 830.2534
#> V57 150.8735
#> V58 -1402.0417
#> V59 467.4052
#> V6 185.1458
#> V60 133.7002
#> V7 -63.4984
#> V8 -249.45
#> V9 470.5011
#> Intercept -142.244
#>
#>
#> Odds Ratios...
#> Class
#> Variable M
#> ===================================
#> V1 Infinity
#> V10 0
#> V11 1.8524621145434497E36
#> V12 3.3286624920175014E72
#> V13 0
#> V14 9.016619752928251E30
#> V15 0
#> V16 0
#> V17 0.0004
#> V18 0
#> V19 1.9339286628524428E73
#> V2 0
#> V20 0
#> V21 0
#> V22 1.48547045633874E48
#> V23 7.936634636466056E14
#> V24 6.155091140250947E34
#> V25 0
#> V26 3.649270841099657E10
#> V27 1.149444436762069E38
#> V28 0
#> V29 0
#> V3 1.3849517738125687E50
#> V30 5.7115176099081604E75
#> V31 0
#> V32 1.299840002684068E84
#> V33 6.495037630609116E42
#> V34 0
#> V35 0
#> V36 3.1593635566283634E26
#> V37 0
#> V38 4.5022615293913675E54
#> V39 6.612593928340354E58
#> V4 2.0938635053567403E177
#> V40 0
#> V41 0
#> V42 0
#> V43 5.655496180545953E84
#> V44 0
#> V45 1.8345435254840942E39
#> V46 5.481675045413893E87
#> V47 2.2808526415610437E99
#> V48 134666.945
#> V49 Infinity
#> V5 9.218956399732878E76
#> V50 0
#> V51 Infinity
#> V52 Infinity
#> V53 1.0334831174352236E56
#> V54 2.2439708069062713E54
#> V55 0
#> V56 Infinity
#> V57 3.3382746950768042E65
#> V58 0
#> V59 9.806185481240175E202
#> V6 2.5574622415631266E80
#> V60 1.1621281850386296E58
#> V7 0
#> V8 0
#> V9 2.1679236360677775E204
#>
# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)
# Score the predictions
predictions$score()
#> classif.ce
#> 0.2608696