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 754.0393
#> V10 -34.5664
#> V11 -46.1694
#> V12 221.8324
#> V13 -32.9615
#> V14 -110.494
#> V15 342.3106
#> V16 -323.7676
#> V17 -38.7115
#> V18 63.2236
#> V19 19.0392
#> V2 374.7173
#> V20 58.0559
#> V21 -131.5702
#> V22 137.6453
#> V23 -3.4297
#> V24 -65.9397
#> V25 169.1659
#> V26 -197.8882
#> V27 88.239
#> V28 20.9816
#> V29 -137.6524
#> V3 -1084.0678
#> V30 228.6977
#> V31 -206.2764
#> V32 -46.6927
#> V33 122.4121
#> V34 -218.699
#> V35 201.4707
#> V36 -158.1521
#> V37 44.0858
#> V38 18.8432
#> V39 61.3163
#> V4 199.7165
#> V40 -148.1571
#> V41 61.2143
#> V42 -33.8
#> V43 1.8187
#> V44 -17.9925
#> V45 340.5175
#> V46 -126.0779
#> V47 244.4778
#> V48 -26.5862
#> V49 346.9894
#> V5 118.8147
#> V50 -1820.8351
#> V51 -935.1447
#> V52 3032.4364
#> V53 857.2196
#> V54 1884.3235
#> V55 -2977.5808
#> V56 1458.5842
#> V57 360.6232
#> V58 1552.1643
#> V59 -2041.729
#> V6 -145.4042
#> V60 -1115.1768
#> V7 -298.316
#> V8 49.6141
#> V9 135.6714
#> Intercept -17.2596
#>
#>
#> Odds Ratios...
#> Class
#> Variable M
#> ===================================
#> V1 Infinity
#> V10 0
#> V11 0
#> V12 2.1907191156485557E96
#> V13 0
#> V14 0
#> V15 4.6090571992408665E148
#> V16 0
#> V17 0
#> V18 2.8685668255476666E27
#> V19 185615544.2046
#> V2 5.465665112574876E162
#> V20 1.634431610803804E25
#> V21 0
#> V22 6.006156857939386E59
#> V23 0.0324
#> V24 0
#> V25 2.936354419558556E73
#> V26 0
#> V27 2.0975965191445965E38
#> V28 1294779202.2421
#> V29 0
#> V3 0
#> V30 2.0996305741010197E99
#> V31 0
#> V32 0
#> V33 1.4550853541234666E53
#> V34 0
#> V35 3.145066679670405E87
#> V36 0
#> V37 1.400363381072458E19
#> V38 152582338.7419
#> V39 4.259337509876384E26
#> V4 5.442185149792072E86
#> V40 0
#> V41 3.8462075129166284E26
#> V42 0
#> V43 6.1639
#> V44 0
#> V45 7.67138313993939E147
#> V46 0
#> V47 1.4975401344004717E106
#> V48 0
#> V49 4.961280686115532E150
#> V5 3.986298643514246E51
#> V50 0
#> V51 0
#> V52 Infinity
#> V53 Infinity
#> V54 Infinity
#> V55 0
#> V56 Infinity
#> V57 4.136848124632892E156
#> V58 Infinity
#> V59 0
#> V6 0
#> V60 0
#> V7 0
#> V8 3.5247699852006E21
#> V9 8.343038490551579E58
#>
# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)
# Score the predictions
predictions$score()
#> classif.ce
#> 0.3188406