Classification Naive Bayes Learner From Weka
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
Examples
# Define the Learner
learner = mlr3::lrn("classif.naive_bayes_weka")
print(learner)
#> <LearnerClassifNaiveBayesWeka:classif.naive_bayes_weka>: Naive Bayes
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, RWeka
#> * Predict Types: [response], prob
#> * Feature Types: logical, integer, numeric, factor, ordered
#> * Properties: missings, multiclass, twoclass
# Define a Task
task = mlr3::tsk("sonar")
# Create train and test set
ids = mlr3::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 0.0343 0.0228
#> std. dev. 0.0239 0.0134
#> weight sum 76 63
#> precision 0.0009 0.0009
#>
#> V10
#> mean 0.2486 0.1683
#> std. dev. 0.1429 0.1241
#> weight sum 76 63
#> precision 0.0051 0.0051
#>
#> V11
#> mean 0.292 0.1853
#> std. dev. 0.1253 0.1267
#> weight sum 76 63
#> precision 0.0052 0.0052
#>
#> V12
#> mean 0.3011 0.2049
#> std. dev. 0.1257 0.151
#> weight sum 76 63
#> precision 0.005 0.005
#>
#> V13
#> mean 0.3119 0.2335
#> std. dev. 0.1335 0.1537
#> weight sum 76 63
#> precision 0.0051 0.0051
#>
#> V14
#> mean 0.3178 0.2847
#> std. dev. 0.1703 0.1704
#> weight sum 76 63
#> precision 0.0072 0.0072
#>
#> V15
#> mean 0.3315 0.3291
#> std. dev. 0.1985 0.2262
#> weight sum 76 63
#> precision 0.0073 0.0073
#>
#> V16
#> mean 0.3826 0.4057
#> std. dev. 0.2113 0.2643
#> weight sum 76 63
#> precision 0.007 0.007
#>
#> V17
#> mean 0.4128 0.4442
#> std. dev. 0.2423 0.2941
#> weight sum 76 63
#> precision 0.0071 0.0071
#>
#> V18
#> mean 0.4597 0.461
#> std. dev. 0.2576 0.2737
#> weight sum 76 63
#> precision 0.0071 0.0071
#>
#> V19
#> mean 0.5478 0.473
#> std. dev. 0.2506 0.2585
#> weight sum 76 63
#> precision 0.0069 0.0069
#>
#> V2
#> mean 0.0461 0.032
#> std. dev. 0.0342 0.0258
#> weight sum 76 63
#> precision 0.0013 0.0013
#>
#> V20
#> mean 0.6363 0.503
#> std. dev. 0.2484 0.238
#> weight sum 76 63
#> precision 0.0067 0.0067
#>
#> V21
#> mean 0.6827 0.5487
#> std. dev. 0.23 0.2359
#> weight sum 76 63
#> precision 0.0071 0.0071
#>
#> V22
#> mean 0.6863 0.596
#> std. dev. 0.2219 0.2497
#> weight sum 76 63
#> precision 0.0069 0.0069
#>
#> V23
#> mean 0.6834 0.6244
#> std. dev. 0.2568 0.2424
#> weight sum 76 63
#> precision 0.0071 0.0071
#>
#> V24
#> mean 0.6795 0.6405
#> std. dev. 0.2581 0.2393
#> weight sum 76 63
#> precision 0.0074 0.0074
#>
#> V25
#> mean 0.6734 0.6632
#> std. dev. 0.2584 0.244
#> weight sum 76 63
#> precision 0.0072 0.0072
#>
#> V26
#> mean 0.7014 0.6912
#> std. dev. 0.2436 0.2345
#> weight sum 76 63
#> precision 0.0069 0.0069
#>
#> V27
#> mean 0.7019 0.6956
#> std. dev. 0.2742 0.206
#> weight sum 76 63
#> precision 0.0076 0.0076
#>
#> V28
#> mean 0.6972 0.6739
#> std. dev. 0.2596 0.2
#> weight sum 76 63
#> precision 0.0071 0.0071
#>
#> V29
#> mean 0.6316 0.6328
#> std. dev. 0.2535 0.219
#> weight sum 76 63
#> precision 0.0074 0.0074
#>
#> V3
#> mean 0.0485 0.0399
#> std. dev. 0.0376 0.0315
#> weight sum 76 63
#> precision 0.0015 0.0015
#>
#> V30
#> mean 0.5685 0.5924
#> std. dev. 0.2118 0.2092
#> weight sum 76 63
#> precision 0.0064 0.0064
#>
#> V31
#> mean 0.467 0.5362
#> std. dev. 0.2098 0.1895
#> weight sum 76 63
#> precision 0.0066 0.0066
#>
#> V32
#> mean 0.4132 0.4403
#> std. dev. 0.2026 0.196
#> weight sum 76 63
#> precision 0.0063 0.0063
#>
#> V33
#> mean 0.3988 0.4162
#> std. dev. 0.1973 0.2027
#> weight sum 76 63
#> precision 0.007 0.007
#>
#> V34
#> mean 0.3701 0.418
#> std. dev. 0.2119 0.2548
#> weight sum 76 63
#> precision 0.0067 0.0067
#>
#> V35
#> mean 0.3337 0.4393
#> std. dev. 0.2497 0.2738
#> weight sum 76 63
#> precision 0.0072 0.0072
#>
#> V36
#> mean 0.3079 0.4574
#> std. dev. 0.2498 0.2729
#> weight sum 76 63
#> precision 0.0072 0.0072
#>
#> V37
#> mean 0.3028 0.4098
#> std. dev. 0.23 0.2388
#> weight sum 76 63
#> precision 0.0066 0.0066
#>
#> V38
#> mean 0.3219 0.3424
#> std. dev. 0.2118 0.2235
#> weight sum 76 63
#> precision 0.007 0.007
#>
#> V39
#> mean 0.3318 0.3105
#> std. dev. 0.1914 0.2174
#> weight sum 76 63
#> precision 0.0069 0.0069
#>
#> V4
#> mean 0.0626 0.043
#> std. dev. 0.0448 0.0347
#> weight sum 76 63
#> precision 0.002 0.002
#>
#> V40
#> mean 0.2915 0.3218
#> std. dev. 0.1645 0.1975
#> weight sum 76 63
#> precision 0.0066 0.0066
#>
#> V41
#> mean 0.2666 0.2872
#> std. dev. 0.1586 0.1653
#> weight sum 76 63
#> precision 0.0055 0.0055
#>
#> V42
#> mean 0.2835 0.2459
#> std. dev. 0.1609 0.1544
#> weight sum 76 63
#> precision 0.0059 0.0059
#>
#> V43
#> mean 0.2657 0.2076
#> std. dev. 0.1272 0.1287
#> weight sum 76 63
#> precision 0.0056 0.0056
#>
#> V44
#> mean 0.227 0.1781
#> std. dev. 0.1323 0.1131
#> weight sum 76 63
#> precision 0.0058 0.0058
#>
#> V45
#> mean 0.2285 0.1413
#> std. dev. 0.162 0.0935
#> weight sum 76 63
#> precision 0.0048 0.0048
#>
#> V46
#> mean 0.1846 0.1111
#> std. dev. 0.1404 0.0862
#> weight sum 76 63
#> precision 0.0046 0.0046
#>
#> V47
#> mean 0.1362 0.0951
#> std. dev. 0.0826 0.062
#> weight sum 76 63
#> precision 0.0032 0.0032
#>
#> V48
#> mean 0.104 0.0704
#> std. dev. 0.062 0.0451
#> weight sum 76 63
#> precision 0.0021 0.0021
#>
#> V49
#> mean 0.0608 0.0371
#> std. dev. 0.0346 0.0261
#> weight sum 76 63
#> precision 0.0014 0.0014
#>
#> V5
#> mean 0.0849 0.0666
#> std. dev. 0.0557 0.0509
#> weight sum 76 63
#> precision 0.0024 0.0024
#>
#> V50
#> mean 0.0224 0.0164
#> std. dev. 0.0149 0.0111
#> weight sum 76 63
#> precision 0.0008 0.0008
#>
#> V51
#> mean 0.0181 0.0129
#> std. dev. 0.0118 0.0084
#> weight sum 76 63
#> precision 0.0007 0.0007
#>
#> V52
#> mean 0.016 0.0103
#> std. dev. 0.0099 0.0071
#> weight sum 76 63
#> precision 0.0004 0.0004
#>
#> V53
#> mean 0.0117 0.01
#> std. dev. 0.0079 0.0065
#> weight sum 76 63
#> precision 0.0004 0.0004
#>
#> V54
#> mean 0.0114 0.0098
#> std. dev. 0.0079 0.005
#> weight sum 76 63
#> precision 0.0003 0.0003
#>
#> V55
#> mean 0.01 0.0081
#> std. dev. 0.0085 0.0043
#> weight sum 76 63
#> precision 0.0004 0.0004
#>
#> V56
#> mean 0.0089 0.008
#> std. dev. 0.0069 0.0049
#> weight sum 76 63
#> precision 0.0004 0.0004
#>
#> V57
#> mean 0.008 0.0079
#> std. dev. 0.0063 0.0055
#> weight sum 76 63
#> precision 0.0004 0.0004
#>
#> V58
#> mean 0.0094 0.0072
#> std. dev. 0.0081 0.0052
#> weight sum 76 63
#> precision 0.0005 0.0005
#>
#> V59
#> mean 0.0081 0.0074
#> std. dev. 0.0058 0.0053
#> weight sum 76 63
#> precision 0.0003 0.0003
#>
#> V6
#> mean 0.1103 0.1029
#> std. dev. 0.0561 0.0678
#> weight sum 76 63
#> precision 0.0028 0.0028
#>
#> V60
#> mean 0.0065 0.0064
#> std. dev. 0.0042 0.004
#> weight sum 76 63
#> precision 0.0002 0.0002
#>
#> V7
#> mean 0.1274 0.1207
#> std. dev. 0.0619 0.0665
#> weight sum 76 63
#> precision 0.0028 0.0028
#>
#> V8
#> mean 0.1523 0.1274
#> std. dev. 0.0883 0.0849
#> weight sum 76 63
#> precision 0.0034 0.0034
#>
#> V9
#> mean 0.2106 0.1465
#> std. dev. 0.1271 0.1099
#> 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
#> 0.2028986