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 0.0344 0.0228
#> std. dev. 0.0264 0.0145
#> weight sum 76 63
#> precision 0.0011 0.0011
#>
#> V10
#> mean 0.2466 0.1525
#> std. dev. 0.1301 0.1012
#> weight sum 76 63
#> precision 0.0047 0.0047
#>
#> V11
#> mean 0.2883 0.1654
#> std. dev. 0.114 0.1013
#> weight sum 76 63
#> precision 0.0044 0.0044
#>
#> V12
#> mean 0.296 0.1854
#> std. dev. 0.1254 0.1227
#> weight sum 76 63
#> precision 0.0046 0.0046
#>
#> V13
#> mean 0.3094 0.2284
#> std. dev. 0.1383 0.1386
#> weight sum 76 63
#> precision 0.0052 0.0052
#>
#> V14
#> mean 0.3185 0.2677
#> std. dev. 0.1647 0.172
#> weight sum 76 63
#> precision 0.0072 0.0072
#>
#> V15
#> mean 0.3249 0.301
#> std. dev. 0.188 0.214
#> weight sum 76 63
#> precision 0.0074 0.0074
#>
#> V16
#> mean 0.3734 0.3766
#> std. dev. 0.2051 0.2518
#> weight sum 76 63
#> precision 0.0072 0.0072
#>
#> V17
#> mean 0.4161 0.42
#> std. dev. 0.2238 0.2819
#> weight sum 76 63
#> precision 0.0071 0.0071
#>
#> V18
#> mean 0.4538 0.4499
#> std. dev. 0.2448 0.2706
#> weight sum 76 63
#> precision 0.0071 0.0071
#>
#> V19
#> mean 0.5345 0.4635
#> std. dev. 0.251 0.2686
#> weight sum 76 63
#> precision 0.007 0.007
#>
#> V2
#> mean 0.0436 0.0308
#> std. dev. 0.0344 0.0262
#> weight sum 76 63
#> precision 0.0013 0.0013
#>
#> V20
#> mean 0.6235 0.5054
#> std. dev. 0.2447 0.2687
#> weight sum 76 63
#> precision 0.007 0.007
#>
#> V21
#> mean 0.6836 0.5438
#> std. dev. 0.2367 0.2539
#> weight sum 76 63
#> precision 0.0072 0.0072
#>
#> V22
#> mean 0.6921 0.5517
#> std. dev. 0.2249 0.2631
#> weight sum 76 63
#> precision 0.0068 0.0068
#>
#> V23
#> mean 0.6857 0.6043
#> std. dev. 0.2428 0.242
#> weight sum 76 63
#> precision 0.007 0.007
#>
#> V24
#> mean 0.693 0.6532
#> std. dev. 0.2328 0.2327
#> weight sum 76 63
#> precision 0.0071 0.0071
#>
#> V25
#> mean 0.6919 0.6681
#> std. dev. 0.241 0.2444
#> weight sum 76 63
#> precision 0.0071 0.0071
#>
#> V26
#> mean 0.7389 0.6955
#> std. dev. 0.2252 0.2433
#> weight sum 76 63
#> precision 0.0071 0.0071
#>
#> V27
#> mean 0.7607 0.6981
#> std. dev. 0.2461 0.2329
#> weight sum 76 63
#> precision 0.0077 0.0077
#>
#> V28
#> mean 0.7494 0.6855
#> std. dev. 0.2372 0.2166
#> weight sum 76 63
#> precision 0.0072 0.0072
#>
#> V29
#> mean 0.6771 0.627
#> std. dev. 0.2237 0.2375
#> weight sum 76 63
#> precision 0.007 0.007
#>
#> V3
#> mean 0.0481 0.0341
#> std. dev. 0.0364 0.0306
#> weight sum 76 63
#> precision 0.0015 0.0015
#>
#> V30
#> mean 0.6042 0.5527
#> std. dev. 0.1969 0.234
#> weight sum 76 63
#> precision 0.007 0.007
#>
#> V31
#> mean 0.4963 0.5251
#> std. dev. 0.2135 0.2002
#> weight sum 76 63
#> precision 0.0066 0.0066
#>
#> V32
#> mean 0.4426 0.4604
#> std. dev. 0.2069 0.223
#> weight sum 76 63
#> precision 0.0064 0.0064
#>
#> V33
#> mean 0.3957 0.425
#> std. dev. 0.1865 0.2248
#> weight sum 76 63
#> precision 0.0067 0.0067
#>
#> V34
#> mean 0.3513 0.4203
#> std. dev. 0.1941 0.2486
#> weight sum 76 63
#> precision 0.0068 0.0068
#>
#> V35
#> mean 0.3232 0.4415
#> std. dev. 0.2454 0.2483
#> weight sum 76 63
#> precision 0.0071 0.0071
#>
#> V36
#> mean 0.3057 0.4408
#> std. dev. 0.2493 0.248
#> weight sum 76 63
#> precision 0.0071 0.0071
#>
#> V37
#> mean 0.3052 0.3989
#> std. dev. 0.2213 0.2446
#> weight sum 76 63
#> precision 0.0066 0.0066
#>
#> V38
#> mean 0.3237 0.3413
#> std. dev. 0.1952 0.2329
#> weight sum 76 63
#> precision 0.007 0.007
#>
#> V39
#> mean 0.3287 0.3139
#> std. dev. 0.1826 0.2185
#> weight sum 76 63
#> precision 0.0069 0.0069
#>
#> V4
#> mean 0.0622 0.0406
#> std. dev. 0.0439 0.031
#> weight sum 76 63
#> precision 0.002 0.002
#>
#> V40
#> mean 0.2893 0.3127
#> std. dev. 0.1482 0.206
#> weight sum 76 63
#> precision 0.0067 0.0067
#>
#> V41
#> mean 0.2801 0.2789
#> std. dev. 0.1541 0.1755
#> weight sum 76 63
#> precision 0.0063 0.0063
#>
#> V42
#> mean 0.2972 0.2393
#> std. dev. 0.1749 0.1627
#> weight sum 76 63
#> precision 0.0059 0.0059
#>
#> V43
#> mean 0.285 0.1978
#> std. dev. 0.1366 0.1166
#> weight sum 76 63
#> precision 0.0044 0.0044
#>
#> V44
#> mean 0.2569 0.1614
#> std. dev. 0.1386 0.0872
#> weight sum 76 63
#> precision 0.004 0.004
#>
#> V45
#> mean 0.2514 0.1359
#> std. dev. 0.1824 0.0856
#> weight sum 76 63
#> precision 0.0049 0.0049
#>
#> V46
#> mean 0.198 0.1153
#> std. dev. 0.1533 0.0883
#> weight sum 76 63
#> precision 0.0047 0.0047
#>
#> V47
#> mean 0.1383 0.0873
#> std. dev. 0.0848 0.0646
#> weight sum 76 63
#> precision 0.0031 0.0031
#>
#> V48
#> mean 0.1043 0.0649
#> std. dev. 0.0574 0.046
#> weight sum 76 63
#> precision 0.0021 0.0021
#>
#> V49
#> mean 0.064 0.0358
#> std. dev. 0.0326 0.0296
#> weight sum 76 63
#> precision 0.0015 0.0015
#>
#> V5
#> mean 0.083 0.06
#> std. dev. 0.0518 0.047
#> weight sum 76 63
#> precision 0.0024 0.0024
#>
#> V50
#> mean 0.0232 0.0178
#> std. dev. 0.0148 0.0129
#> weight sum 76 63
#> precision 0.0007 0.0007
#>
#> V51
#> mean 0.0186 0.0119
#> std. dev. 0.0114 0.0081
#> weight sum 76 63
#> precision 0.0007 0.0007
#>
#> V52
#> mean 0.0153 0.0101
#> std. dev. 0.0097 0.0062
#> weight sum 76 63
#> precision 0.0004 0.0004
#>
#> V53
#> mean 0.0112 0.0091
#> std. dev. 0.0077 0.006
#> weight sum 76 63
#> precision 0.0004 0.0004
#>
#> V54
#> mean 0.0121 0.0094
#> std. dev. 0.0083 0.0056
#> weight sum 76 63
#> precision 0.0003 0.0003
#>
#> V55
#> mean 0.0098 0.0083
#> std. dev. 0.0088 0.0051
#> weight sum 76 63
#> precision 0.0004 0.0004
#>
#> V56
#> mean 0.0088 0.0068
#> std. dev. 0.0061 0.0042
#> weight sum 76 63
#> precision 0.0004 0.0004
#>
#> V57
#> mean 0.0075 0.0068
#> std. dev. 0.006 0.0043
#> weight sum 76 63
#> precision 0.0004 0.0004
#>
#> V58
#> mean 0.0095 0.0063
#> std. dev. 0.0082 0.0044
#> weight sum 76 63
#> precision 0.0005 0.0005
#>
#> V59
#> mean 0.0083 0.0066
#> std. dev. 0.0062 0.0047
#> weight sum 76 63
#> precision 0.0004 0.0004
#>
#> V6
#> mean 0.1084 0.0944
#> std. dev. 0.0487 0.0702
#> weight sum 76 63
#> precision 0.0028 0.0028
#>
#> V60
#> mean 0.0063 0.0057
#> std. dev. 0.0045 0.0031
#> weight sum 76 63
#> precision 0.0003 0.0003
#>
#> V7
#> mean 0.1218 0.1102
#> std. dev. 0.0539 0.0674
#> weight sum 76 63
#> precision 0.0028 0.0028
#>
#> V8
#> mean 0.1501 0.1089
#> std. dev. 0.0831 0.0725
#> weight sum 76 63
#> precision 0.0031 0.0031
#>
#> V9
#> mean 0.2163 0.1329
#> std. dev. 0.1172 0.0862
#> weight sum 76 63
#> precision 0.005 0.005
#>
#>
# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)
# Score the predictions
predictions$score()
#> classif.ce
#> 0.3188406