Classification Naive Bayes Learner From Weka
Source:R/learner_RWeka_classif_naive_bayes_weka.R
mlr_learners_classif.naive_bayes_weka.RdNaive Bayes Classifier Using Estimator Classes.
Calls RWeka::make_Weka_classifier() 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 | - | - | |
| K | logical | FALSE | TRUE, FALSE | - |
| D | logical | FALSE | TRUE, FALSE | - |
| O | logical | FALSE | TRUE, FALSE | - |
| 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
John GH, Langley P (1995). “Estimating Continuous Distributions in Bayesian Classifiers.” In Eleventh Conference on Uncertainty in Artificial Intelligence, 338-345.
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 -> LearnerClassifNaiveBayesWeka
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.naive_bayes_weka")
print(learner)
#>
#> ── <LearnerClassifNaiveBayesWeka> (classif.naive_bayes_weka): Naive Bayes ──────
#> • 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)
#> Naive Bayes Classifier
#>
#> Class
#> Attribute M R
#> (0.5) (0.5)
#> ===============================
#> V1
#> mean 0.0378 0.0229
#> std. dev. 0.0273 0.0162
#> weight sum 70 69
#> precision 0.0011 0.0011
#>
#> V10
#> mean 0.2543 0.1564
#> std. dev. 0.1368 0.1043
#> weight sum 70 69
#> precision 0.0051 0.0051
#>
#> V11
#> mean 0.2932 0.1741
#> std. dev. 0.1415 0.1085
#> weight sum 70 69
#> precision 0.0051 0.0051
#>
#> V12
#> mean 0.3078 0.1907
#> std. dev. 0.1339 0.1294
#> weight sum 70 69
#> precision 0.0046 0.0046
#>
#> V13
#> mean 0.3231 0.2242
#> std. dev. 0.1327 0.1372
#> weight sum 70 69
#> precision 0.0052 0.0052
#>
#> V14
#> mean 0.3262 0.2589
#> std. dev. 0.1688 0.1641
#> weight sum 70 69
#> precision 0.0071 0.0071
#>
#> V15
#> mean 0.3382 0.2905
#> std. dev. 0.1957 0.2069
#> weight sum 70 69
#> precision 0.0067 0.0067
#>
#> V16
#> mean 0.389 0.3549
#> std. dev. 0.2188 0.2494
#> weight sum 70 69
#> precision 0.007 0.007
#>
#> V17
#> mean 0.4252 0.4031
#> std. dev. 0.2489 0.2896
#> weight sum 70 69
#> precision 0.0072 0.0072
#>
#> V18
#> mean 0.4596 0.4388
#> std. dev. 0.2548 0.2672
#> weight sum 70 69
#> precision 0.0067 0.0067
#>
#> V19
#> mean 0.5473 0.4351
#> std. dev. 0.246 0.2487
#> weight sum 70 69
#> precision 0.0069 0.0069
#>
#> V2
#> mean 0.0477 0.0305
#> std. dev. 0.0395 0.0252
#> weight sum 70 69
#> precision 0.0019 0.0019
#>
#> V20
#> mean 0.6177 0.466
#> std. dev. 0.2467 0.2406
#> weight sum 70 69
#> precision 0.0069 0.0069
#>
#> V21
#> mean 0.6661 0.5184
#> std. dev. 0.2346 0.2409
#> weight sum 70 69
#> precision 0.0071 0.0071
#>
#> V22
#> mean 0.6915 0.5571
#> std. dev. 0.2252 0.2537
#> weight sum 70 69
#> precision 0.0069 0.0069
#>
#> V23
#> mean 0.7027 0.6177
#> std. dev. 0.2333 0.2468
#> weight sum 70 69
#> precision 0.0071 0.0071
#>
#> V24
#> mean 0.7052 0.6678
#> std. dev. 0.2348 0.2267
#> weight sum 70 69
#> precision 0.0071 0.0071
#>
#> V25
#> mean 0.6942 0.6811
#> std. dev. 0.2388 0.2378
#> weight sum 70 69
#> precision 0.0073 0.0073
#>
#> V26
#> mean 0.7044 0.694
#> std. dev. 0.2402 0.23
#> weight sum 70 69
#> precision 0.0065 0.0065
#>
#> V27
#> mean 0.71 0.674
#> std. dev. 0.2595 0.2115
#> weight sum 70 69
#> precision 0.0071 0.0071
#>
#> V28
#> mean 0.7089 0.6587
#> std. dev. 0.2607 0.2126
#> weight sum 70 69
#> precision 0.0073 0.0073
#>
#> V29
#> mean 0.6429 0.6348
#> std. dev. 0.2491 0.2475
#> weight sum 70 69
#> precision 0.0076 0.0076
#>
#> V3
#> mean 0.0524 0.0359
#> std. dev. 0.0506 0.0272
#> weight sum 70 69
#> precision 0.0024 0.0024
#>
#> V30
#> mean 0.5682 0.5847
#> std. dev. 0.209 0.2303
#> weight sum 70 69
#> precision 0.0069 0.0069
#>
#> V31
#> mean 0.4861 0.537
#> std. dev. 0.213 0.1952
#> weight sum 70 69
#> precision 0.0063 0.0063
#>
#> V32
#> mean 0.4271 0.4738
#> std. dev. 0.2076 0.2133
#> weight sum 70 69
#> precision 0.0064 0.0064
#>
#> V33
#> mean 0.3975 0.4664
#> std. dev. 0.1883 0.2237
#> weight sum 70 69
#> precision 0.0068 0.0068
#>
#> V34
#> mean 0.3567 0.4524
#> std. dev. 0.1905 0.2538
#> weight sum 70 69
#> precision 0.0068 0.0068
#>
#> V35
#> mean 0.3127 0.4569
#> std. dev. 0.2349 0.2622
#> weight sum 70 69
#> precision 0.0072 0.0072
#>
#> V36
#> mean 0.2957 0.464
#> std. dev. 0.2292 0.2627
#> weight sum 70 69
#> precision 0.0073 0.0073
#>
#> V37
#> mean 0.2945 0.4112
#> std. dev. 0.2204 0.2528
#> weight sum 70 69
#> precision 0.0067 0.0067
#>
#> V38
#> mean 0.3081 0.3385
#> std. dev. 0.1879 0.2338
#> weight sum 70 69
#> precision 0.007 0.007
#>
#> V39
#> mean 0.315 0.2959
#> std. dev. 0.1759 0.2171
#> weight sum 70 69
#> precision 0.0069 0.0069
#>
#> V4
#> mean 0.0683 0.04
#> std. dev. 0.0622 0.0302
#> weight sum 70 69
#> precision 0.0034 0.0034
#>
#> V40
#> mean 0.2872 0.3107
#> std. dev. 0.158 0.1882
#> weight sum 70 69
#> precision 0.0066 0.0066
#>
#> V41
#> mean 0.2742 0.2851
#> std. dev. 0.1621 0.1766
#> weight sum 70 69
#> precision 0.0064 0.0064
#>
#> V42
#> mean 0.2894 0.2563
#> std. dev. 0.1584 0.1697
#> weight sum 70 69
#> precision 0.0056 0.0056
#>
#> V43
#> mean 0.2803 0.2188
#> std. dev. 0.1422 0.1243
#> weight sum 70 69
#> precision 0.0055 0.0055
#>
#> V44
#> mean 0.2556 0.1776
#> std. dev. 0.1474 0.0898
#> weight sum 70 69
#> precision 0.0043 0.0043
#>
#> V45
#> mean 0.242 0.1406
#> std. dev. 0.1743 0.0918
#> weight sum 70 69
#> precision 0.0052 0.0052
#>
#> V46
#> mean 0.1935 0.1203
#> std. dev. 0.1565 0.0943
#> weight sum 70 69
#> precision 0.0055 0.0055
#>
#> V47
#> mean 0.1507 0.0944
#> std. dev. 0.1024 0.0704
#> weight sum 70 69
#> precision 0.0041 0.0041
#>
#> V48
#> mean 0.1128 0.0679
#> std. dev. 0.0688 0.0503
#> weight sum 70 69
#> precision 0.0024 0.0024
#>
#> V49
#> mean 0.0635 0.0389
#> std. dev. 0.0372 0.0324
#> weight sum 70 69
#> precision 0.0015 0.0015
#>
#> V5
#> mean 0.0933 0.0577
#> std. dev. 0.0686 0.0483
#> weight sum 70 69
#> precision 0.003 0.003
#>
#> V50
#> mean 0.0221 0.0182
#> std. dev. 0.0146 0.0135
#> weight sum 70 69
#> precision 0.0008 0.0008
#>
#> V51
#> mean 0.0193 0.013
#> std. dev. 0.015 0.0091
#> weight sum 70 69
#> precision 0.0008 0.0008
#>
#> V52
#> mean 0.0164 0.0106
#> std. dev. 0.0119 0.0076
#> weight sum 70 69
#> precision 0.0007 0.0007
#>
#> V53
#> mean 0.0118 0.0093
#> std. dev. 0.0076 0.0055
#> weight sum 70 69
#> precision 0.0003 0.0003
#>
#> V54
#> mean 0.0118 0.009
#> std. dev. 0.0082 0.0051
#> weight sum 70 69
#> precision 0.0003 0.0003
#>
#> V55
#> mean 0.01 0.0081
#> std. dev. 0.009 0.0048
#> weight sum 70 69
#> precision 0.0004 0.0004
#>
#> V56
#> mean 0.0084 0.0073
#> std. dev. 0.0063 0.0045
#> weight sum 70 69
#> precision 0.0004 0.0004
#>
#> V57
#> mean 0.0079 0.0078
#> std. dev. 0.0061 0.0054
#> weight sum 70 69
#> precision 0.0004 0.0004
#>
#> V58
#> mean 0.0094 0.0064
#> std. dev. 0.0077 0.0047
#> weight sum 70 69
#> precision 0.0005 0.0005
#>
#> V59
#> mean 0.0093 0.0069
#> std. dev. 0.0072 0.0042
#> weight sum 70 69
#> precision 0.0004 0.0004
#>
#> V6
#> mean 0.1139 0.0951
#> std. dev. 0.0547 0.0679
#> weight sum 70 69
#> precision 0.0028 0.0028
#>
#> V60
#> mean 0.0069 0.0061
#> std. dev. 0.0064 0.0034
#> weight sum 70 69
#> precision 0.0005 0.0005
#>
#> V7
#> mean 0.126 0.1155
#> std. dev. 0.0602 0.0683
#> weight sum 70 69
#> precision 0.0027 0.0027
#>
#> V8
#> mean 0.1439 0.1186
#> std. dev. 0.0828 0.0808
#> weight sum 70 69
#> precision 0.0034 0.0034
#>
#> V9
#> mean 0.2073 0.1354
#> std. dev. 0.1139 0.0952
#> weight sum 70 69
#> precision 0.0049 0.0049
#>
#>
# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)
# Score the predictions
predictions$score()
#> classif.ce
#> 0.3043478