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/chapters/chapter2/data_and_basic_modeling.html#sec-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()
LearnerClassifNaiveBayesWeka$marshal()
Marshal the learner's model.
Arguments
...(any)
Additional arguments passed tomlr3::marshal_model().
LearnerClassifNaiveBayesWeka$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', predict_raw = 'FALSE'
# 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
#> (0.55) (0.45)
#> ===============================
#> V1
#> mean 0.0354 0.0242
#> std. dev. 0.0279 0.0164
#> weight sum 76 63
#> precision 0.0011 0.0011
#>
#> V10
#> mean 0.2519 0.1728
#> std. dev. 0.1452 0.122
#> weight sum 76 63
#> precision 0.0051 0.0051
#>
#> V11
#> mean 0.2871 0.1875
#> std. dev. 0.1315 0.1201
#> weight sum 76 63
#> precision 0.0052 0.0052
#>
#> V12
#> mean 0.2955 0.2106
#> std. dev. 0.1189 0.1411
#> weight sum 76 63
#> precision 0.0049 0.0049
#>
#> V13
#> mean 0.3108 0.243
#> std. dev. 0.1264 0.1406
#> weight sum 76 63
#> precision 0.0052 0.0052
#>
#> V14
#> mean 0.3171 0.2777
#> std. dev. 0.1693 0.1655
#> weight sum 76 63
#> precision 0.0071 0.0071
#>
#> V15
#> mean 0.3189 0.3118
#> std. dev. 0.2009 0.2166
#> weight sum 76 63
#> precision 0.0067 0.0067
#>
#> V16
#> mean 0.371 0.3849
#> std. dev. 0.2183 0.253
#> weight sum 76 63
#> precision 0.0071 0.0071
#>
#> V17
#> mean 0.4081 0.4292
#> std. dev. 0.2408 0.2872
#> weight sum 76 63
#> precision 0.0071 0.0071
#>
#> V18
#> mean 0.453 0.462
#> std. dev. 0.2543 0.2645
#> weight sum 76 63
#> precision 0.0068 0.0068
#>
#> V19
#> mean 0.5352 0.4669
#> std. dev. 0.2531 0.2515
#> weight sum 76 63
#> precision 0.0069 0.0069
#>
#> V2
#> mean 0.0467 0.0312
#> std. dev. 0.0417 0.0253
#> weight sum 76 63
#> precision 0.0019 0.0019
#>
#> V20
#> mean 0.6106 0.4918
#> std. dev. 0.2605 0.2456
#> weight sum 76 63
#> precision 0.0069 0.0069
#>
#> V21
#> mean 0.6536 0.539
#> std. dev. 0.2603 0.2357
#> weight sum 76 63
#> precision 0.0071 0.0071
#>
#> V22
#> mean 0.6617 0.5581
#> std. dev. 0.2372 0.2591
#> weight sum 76 63
#> precision 0.0072 0.0072
#>
#> V23
#> mean 0.6808 0.5996
#> std. dev. 0.2515 0.2331
#> weight sum 76 63
#> precision 0.0071 0.0071
#>
#> V24
#> mean 0.6931 0.6502
#> std. dev. 0.2507 0.228
#> weight sum 76 63
#> precision 0.0073 0.0073
#>
#> V25
#> mean 0.6911 0.6703
#> std. dev. 0.2353 0.2527
#> weight sum 76 63
#> precision 0.0074 0.0074
#>
#> V26
#> mean 0.7127 0.6798
#> std. dev. 0.2174 0.2447
#> weight sum 76 63
#> precision 0.0064 0.0064
#>
#> V27
#> mean 0.7106 0.6759
#> std. dev. 0.2522 0.2216
#> weight sum 76 63
#> precision 0.0072 0.0072
#>
#> V28
#> mean 0.7166 0.674
#> std. dev. 0.2518 0.2033
#> weight sum 76 63
#> precision 0.0075 0.0075
#>
#> V29
#> mean 0.6582 0.6424
#> std. dev. 0.2384 0.2307
#> weight sum 76 63
#> precision 0.0074 0.0074
#>
#> V3
#> mean 0.0535 0.037
#> std. dev. 0.0494 0.0303
#> weight sum 76 63
#> precision 0.0023 0.0023
#>
#> V30
#> mean 0.6037 0.576
#> std. dev. 0.2076 0.2275
#> weight sum 76 63
#> precision 0.0068 0.0068
#>
#> V31
#> mean 0.5094 0.5279
#> std. dev. 0.2279 0.2065
#> weight sum 76 63
#> precision 0.0061 0.0061
#>
#> V32
#> mean 0.4348 0.4529
#> std. dev. 0.2269 0.2154
#> weight sum 76 63
#> precision 0.0065 0.0065
#>
#> V33
#> mean 0.398 0.4374
#> std. dev. 0.211 0.2172
#> weight sum 76 63
#> precision 0.007 0.007
#>
#> V34
#> mean 0.3799 0.4419
#> std. dev. 0.215 0.2492
#> weight sum 76 63
#> precision 0.0068 0.0068
#>
#> V35
#> mean 0.3591 0.4541
#> std. dev. 0.2414 0.2483
#> weight sum 76 63
#> precision 0.0071 0.0071
#>
#> V36
#> mean 0.3329 0.4561
#> std. dev. 0.2524 0.2486
#> weight sum 76 63
#> precision 0.0072 0.0072
#>
#> V37
#> mean 0.3296 0.4113
#> std. dev. 0.2261 0.2386
#> weight sum 76 63
#> precision 0.0066 0.0066
#>
#> V38
#> mean 0.3337 0.3505
#> std. dev. 0.199 0.2333
#> weight sum 76 63
#> precision 0.007 0.007
#>
#> V39
#> mean 0.3358 0.3275
#> std. dev. 0.1694 0.2316
#> weight sum 76 63
#> precision 0.0068 0.0068
#>
#> V4
#> mean 0.0698 0.0442
#> std. dev. 0.0607 0.0335
#> weight sum 76 63
#> precision 0.0033 0.0033
#>
#> V40
#> mean 0.3111 0.3406
#> std. dev. 0.1597 0.2099
#> weight sum 76 63
#> precision 0.0067 0.0067
#>
#> V41
#> mean 0.3116 0.3028
#> std. dev. 0.1691 0.1881
#> weight sum 76 63
#> precision 0.0063 0.0063
#>
#> V42
#> mean 0.3153 0.2664
#> std. dev. 0.1713 0.1706
#> weight sum 76 63
#> precision 0.0057 0.0057
#>
#> V43
#> mean 0.2905 0.2262
#> std. dev. 0.1424 0.1378
#> weight sum 76 63
#> precision 0.0056 0.0056
#>
#> V44
#> mean 0.2543 0.1888
#> std. dev. 0.1411 0.1214
#> weight sum 76 63
#> precision 0.0058 0.0058
#>
#> V45
#> mean 0.2457 0.1498
#> std. dev. 0.1768 0.1086
#> weight sum 76 63
#> precision 0.0051 0.0051
#>
#> V46
#> mean 0.2047 0.1224
#> std. dev. 0.1662 0.1064
#> weight sum 76 63
#> precision 0.0055 0.0055
#>
#> V47
#> mean 0.1536 0.099
#> std. dev. 0.1024 0.0777
#> weight sum 76 63
#> precision 0.0041 0.0041
#>
#> V48
#> mean 0.1166 0.0717
#> std. dev. 0.0687 0.0533
#> weight sum 76 63
#> precision 0.0024 0.0024
#>
#> V49
#> mean 0.0659 0.0405
#> std. dev. 0.0389 0.0341
#> weight sum 76 63
#> precision 0.0015 0.0015
#>
#> V5
#> mean 0.0896 0.0643
#> std. dev. 0.0644 0.0512
#> weight sum 76 63
#> precision 0.003 0.003
#>
#> V50
#> mean 0.0237 0.0178
#> std. dev. 0.0154 0.0139
#> weight sum 76 63
#> precision 0.0007 0.0007
#>
#> V51
#> mean 0.0198 0.0125
#> std. dev. 0.015 0.0083
#> weight sum 76 63
#> precision 0.0009 0.0009
#>
#> V52
#> mean 0.0172 0.0102
#> std. dev. 0.0115 0.0065
#> weight sum 76 63
#> precision 0.0007 0.0007
#>
#> V53
#> mean 0.0131 0.0094
#> std. dev. 0.008 0.006
#> weight sum 76 63
#> precision 0.0004 0.0004
#>
#> V54
#> mean 0.0121 0.0095
#> std. dev. 0.0087 0.0054
#> weight sum 76 63
#> precision 0.0003 0.0003
#>
#> V55
#> mean 0.0094 0.0084
#> std. dev. 0.0083 0.0055
#> weight sum 76 63
#> precision 0.0005 0.0005
#>
#> V56
#> mean 0.0096 0.0075
#> std. dev. 0.0072 0.0053
#> weight sum 76 63
#> precision 0.0004 0.0004
#>
#> V57
#> mean 0.008 0.0082
#> std. dev. 0.0059 0.0063
#> weight sum 76 63
#> precision 0.0004 0.0004
#>
#> V58
#> mean 0.0092 0.0073
#> std. dev. 0.008 0.0048
#> weight sum 76 63
#> precision 0.0005 0.0005
#>
#> V59
#> mean 0.0085 0.0073
#> std. dev. 0.0061 0.0051
#> weight sum 76 63
#> precision 0.0003 0.0003
#>
#> V6
#> mean 0.1132 0.105
#> std. dev. 0.0528 0.0737
#> weight sum 76 63
#> precision 0.0028 0.0028
#>
#> V60
#> mean 0.0067 0.0061
#> std. dev. 0.0049 0.0037
#> weight sum 76 63
#> precision 0.0003 0.0003
#>
#> V7
#> mean 0.1269 0.1239
#> std. dev. 0.0558 0.0737
#> weight sum 76 63
#> precision 0.0027 0.0027
#>
#> V8
#> mean 0.15 0.135
#> std. dev. 0.0906 0.0865
#> weight sum 76 63
#> precision 0.0034 0.0034
#>
#> V9
#> mean 0.2133 0.1555
#> std. dev. 0.133 0.1113
#> weight sum 76 63
#> 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
#> 0.2753623