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 0.0355 0.0237
#> std. dev. 0.026 0.0147
#> weight sum 76 63
#> precision 0.0009 0.0009
#>
#> V10
#> mean 0.2533 0.1541
#> std. dev. 0.1435 0.1065
#> weight sum 76 63
#> precision 0.0047 0.0047
#>
#> V11
#> mean 0.2848 0.1783
#> std. dev. 0.1214 0.1078
#> weight sum 76 63
#> precision 0.0047 0.0047
#>
#> V12
#> mean 0.2945 0.2061
#> std. dev. 0.113 0.1327
#> weight sum 76 63
#> precision 0.0046 0.0046
#>
#> V13
#> mean 0.3056 0.2415
#> std. dev. 0.1167 0.1454
#> weight sum 76 63
#> precision 0.0051 0.0051
#>
#> V14
#> mean 0.3129 0.2745
#> std. dev. 0.1343 0.1737
#> weight sum 76 63
#> precision 0.0058 0.0058
#>
#> V15
#> mean 0.3275 0.3131
#> std. dev. 0.1765 0.224
#> weight sum 76 63
#> precision 0.0072 0.0072
#>
#> V16
#> mean 0.3748 0.3775
#> std. dev. 0.1954 0.2577
#> weight sum 76 63
#> precision 0.0071 0.0071
#>
#> V17
#> mean 0.4039 0.4123
#> std. dev. 0.2284 0.2858
#> weight sum 76 63
#> precision 0.0071 0.0071
#>
#> V18
#> mean 0.4372 0.4442
#> std. dev. 0.2502 0.2663
#> weight sum 76 63
#> precision 0.007 0.007
#>
#> V19
#> mean 0.513 0.4757
#> std. dev. 0.249 0.262
#> weight sum 76 63
#> precision 0.007 0.007
#>
#> V2
#> mean 0.0452 0.0319
#> std. dev. 0.0359 0.0253
#> weight sum 76 63
#> precision 0.0013 0.0013
#>
#> V20
#> mean 0.6078 0.5158
#> std. dev. 0.2565 0.2639
#> weight sum 76 63
#> precision 0.0069 0.0069
#>
#> V21
#> mean 0.6601 0.5435
#> std. dev. 0.2558 0.2518
#> weight sum 76 63
#> precision 0.0071 0.0071
#>
#> V22
#> mean 0.6473 0.549
#> std. dev. 0.2416 0.2697
#> weight sum 76 63
#> precision 0.0072 0.0072
#>
#> V23
#> mean 0.643 0.5849
#> std. dev. 0.2724 0.253
#> weight sum 76 63
#> precision 0.007 0.007
#>
#> V24
#> mean 0.6657 0.627
#> std. dev. 0.2646 0.2509
#> weight sum 76 63
#> precision 0.0073 0.0073
#>
#> V25
#> mean 0.6757 0.628
#> std. dev. 0.2481 0.2831
#> weight sum 76 63
#> precision 0.0073 0.0073
#>
#> V26
#> mean 0.7152 0.6677
#> std. dev. 0.22 0.2518
#> weight sum 76 63
#> precision 0.0064 0.0064
#>
#> V27
#> mean 0.7235 0.6822
#> std. dev. 0.2544 0.2146
#> weight sum 76 63
#> precision 0.0071 0.0071
#>
#> V28
#> mean 0.7193 0.6814
#> std. dev. 0.2371 0.1927
#> weight sum 76 63
#> precision 0.0062 0.0062
#>
#> V29
#> mean 0.6529 0.6597
#> std. dev. 0.2342 0.2237
#> weight sum 76 63
#> precision 0.0074 0.0074
#>
#> V3
#> mean 0.0527 0.0371
#> std. dev. 0.0393 0.0289
#> weight sum 76 63
#> precision 0.0015 0.0015
#>
#> V30
#> mean 0.6027 0.6038
#> std. dev. 0.2093 0.2241
#> weight sum 76 63
#> precision 0.0069 0.0069
#>
#> V31
#> mean 0.5014 0.5438
#> std. dev. 0.2321 0.1864
#> weight sum 76 63
#> precision 0.0066 0.0066
#>
#> V32
#> mean 0.4502 0.4628
#> std. dev. 0.2155 0.2044
#> weight sum 76 63
#> precision 0.0065 0.0065
#>
#> V33
#> mean 0.4145 0.4737
#> std. dev. 0.1935 0.2033
#> weight sum 76 63
#> precision 0.0069 0.0069
#>
#> V34
#> mean 0.3826 0.4839
#> std. dev. 0.2158 0.2548
#> weight sum 76 63
#> precision 0.0068 0.0068
#>
#> V35
#> mean 0.3614 0.468
#> std. dev. 0.2631 0.273
#> weight sum 76 63
#> precision 0.0071 0.0071
#>
#> V36
#> mean 0.346 0.4688
#> std. dev. 0.2652 0.2724
#> weight sum 76 63
#> precision 0.0071 0.0071
#>
#> V37
#> mean 0.3463 0.4314
#> std. dev. 0.2405 0.2461
#> weight sum 76 63
#> precision 0.0066 0.0066
#>
#> V38
#> mean 0.3635 0.3664
#> std. dev. 0.2181 0.2334
#> weight sum 76 63
#> precision 0.0071 0.0071
#>
#> V39
#> mean 0.3665 0.339
#> std. dev. 0.1915 0.2381
#> weight sum 76 63
#> precision 0.0068 0.0068
#>
#> V4
#> mean 0.0652 0.0428
#> std. dev. 0.0458 0.0325
#> weight sum 76 63
#> precision 0.002 0.002
#>
#> V40
#> mean 0.3235 0.3393
#> std. dev. 0.1662 0.2146
#> weight sum 76 63
#> precision 0.0067 0.0067
#>
#> V41
#> mean 0.314 0.2957
#> std. dev. 0.1664 0.1912
#> weight sum 76 63
#> precision 0.0063 0.0063
#>
#> V42
#> mean 0.3194 0.2678
#> std. dev. 0.1752 0.1748
#> weight sum 76 63
#> precision 0.0059 0.0059
#>
#> V43
#> mean 0.2881 0.2264
#> std. dev. 0.1464 0.1433
#> weight sum 76 63
#> precision 0.0056 0.0056
#>
#> V44
#> mean 0.2639 0.1852
#> std. dev. 0.1474 0.12
#> weight sum 76 63
#> precision 0.0059 0.0059
#>
#> V45
#> mean 0.2703 0.1521
#> std. dev. 0.1846 0.1052
#> weight sum 76 63
#> precision 0.0051 0.0051
#>
#> V46
#> mean 0.2189 0.1246
#> std. dev. 0.1669 0.1053
#> weight sum 76 63
#> precision 0.0055 0.0055
#>
#> V47
#> mean 0.158 0.0988
#> std. dev. 0.1032 0.0742
#> weight sum 76 63
#> precision 0.0041 0.0041
#>
#> V48
#> mean 0.1212 0.0727
#> std. dev. 0.0712 0.0504
#> weight sum 76 63
#> precision 0.0024 0.0024
#>
#> V49
#> mean 0.0676 0.0402
#> std. dev. 0.0392 0.0317
#> weight sum 76 63
#> precision 0.0015 0.0015
#>
#> V5
#> mean 0.0869 0.0659
#> std. dev. 0.0526 0.0498
#> weight sum 76 63
#> precision 0.0025 0.0025
#>
#> V50
#> mean 0.0245 0.0177
#> std. dev. 0.0155 0.0129
#> weight sum 76 63
#> precision 0.0007 0.0007
#>
#> V51
#> mean 0.0202 0.0123
#> std. dev. 0.0154 0.0086
#> weight sum 76 63
#> precision 0.0009 0.0009
#>
#> V52
#> mean 0.0168 0.0094
#> std. dev. 0.0114 0.0061
#> weight sum 76 63
#> precision 0.0006 0.0006
#>
#> V53
#> mean 0.0124 0.0094
#> std. dev. 0.008 0.0063
#> weight sum 76 63
#> precision 0.0004 0.0004
#>
#> V54
#> mean 0.0132 0.0096
#> std. dev. 0.0086 0.0055
#> weight sum 76 63
#> precision 0.0003 0.0003
#>
#> V55
#> mean 0.0102 0.0089
#> std. dev. 0.0086 0.0057
#> weight sum 76 63
#> precision 0.0005 0.0005
#>
#> V56
#> mean 0.0101 0.0077
#> std. dev. 0.0071 0.0045
#> weight sum 76 63
#> precision 0.0004 0.0004
#>
#> V57
#> mean 0.0087 0.0079
#> std. dev. 0.0061 0.0053
#> weight sum 76 63
#> precision 0.0004 0.0004
#>
#> V58
#> mean 0.0092 0.0069
#> std. dev. 0.0078 0.0048
#> weight sum 76 63
#> precision 0.0005 0.0005
#>
#> V59
#> mean 0.0094 0.0073
#> std. dev. 0.0076 0.005
#> weight sum 76 63
#> precision 0.0004 0.0004
#>
#> V6
#> mean 0.1165 0.1033
#> std. dev. 0.0498 0.0686
#> weight sum 76 63
#> precision 0.0027 0.0027
#>
#> V60
#> mean 0.0077 0.006
#> std. dev. 0.0064 0.0037
#> weight sum 76 63
#> precision 0.0005 0.0005
#>
#> V7
#> mean 0.1335 0.1202
#> std. dev. 0.0583 0.0676
#> weight sum 76 63
#> precision 0.0027 0.0027
#>
#> V8
#> mean 0.1571 0.1191
#> std. dev. 0.0953 0.0804
#> weight sum 76 63
#> precision 0.0033 0.0033
#>
#> V9
#> mean 0.2247 0.1347
#> std. dev. 0.1372 0.0994
#> 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
#> 0.3768116