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
#> (0.5) (0.5)
#> ===============================
#> V1
#> mean 0.0346 0.0233
#> std. dev. 0.0218 0.0157
#> weight sum 69 70
#> precision 0.0009 0.0009
#>
#> V10
#> mean 0.2513 0.1551
#> std. dev. 0.1411 0.1216
#> weight sum 69 70
#> precision 0.0051 0.0051
#>
#> V11
#> mean 0.2922 0.1767
#> std. dev. 0.1356 0.1226
#> weight sum 69 70
#> precision 0.0052 0.0052
#>
#> V12
#> mean 0.3063 0.2011
#> std. dev. 0.135 0.1415
#> weight sum 69 70
#> precision 0.005 0.005
#>
#> V13
#> mean 0.3211 0.2306
#> std. dev. 0.1287 0.1388
#> weight sum 69 70
#> precision 0.0049 0.0049
#>
#> V14
#> mean 0.3255 0.2642
#> std. dev. 0.1663 0.1659
#> weight sum 69 70
#> precision 0.0071 0.0071
#>
#> V15
#> mean 0.3307 0.3003
#> std. dev. 0.1996 0.2118
#> weight sum 69 70
#> precision 0.0074 0.0074
#>
#> V16
#> mean 0.3858 0.3765
#> std. dev. 0.2165 0.2515
#> weight sum 69 70
#> precision 0.0073 0.0073
#>
#> V17
#> mean 0.4275 0.4227
#> std. dev. 0.239 0.2861
#> weight sum 69 70
#> precision 0.0071 0.0071
#>
#> V18
#> mean 0.4693 0.4581
#> std. dev. 0.2587 0.2743
#> weight sum 69 70
#> precision 0.007 0.007
#>
#> V19
#> mean 0.5535 0.4874
#> std. dev. 0.2598 0.2608
#> weight sum 69 70
#> precision 0.0069 0.0069
#>
#> V2
#> mean 0.0423 0.0302
#> std. dev. 0.0293 0.0188
#> weight sum 69 70
#> precision 0.001 0.001
#>
#> V20
#> mean 0.6222 0.5097
#> std. dev. 0.2693 0.2686
#> weight sum 69 70
#> precision 0.0069 0.0069
#>
#> V21
#> mean 0.6666 0.5342
#> std. dev. 0.2553 0.2503
#> weight sum 69 70
#> precision 0.0072 0.0072
#>
#> V22
#> mean 0.6863 0.5447
#> std. dev. 0.2352 0.2524
#> weight sum 69 70
#> precision 0.0072 0.0072
#>
#> V23
#> mean 0.6998 0.59
#> std. dev. 0.2433 0.2294
#> weight sum 69 70
#> precision 0.007 0.007
#>
#> V24
#> mean 0.6939 0.6416
#> std. dev. 0.2468 0.2213
#> weight sum 69 70
#> precision 0.0072 0.0072
#>
#> V25
#> mean 0.6716 0.6552
#> std. dev. 0.2506 0.2492
#> weight sum 69 70
#> precision 0.0073 0.0073
#>
#> V26
#> mean 0.6844 0.6697
#> std. dev. 0.2548 0.2438
#> weight sum 69 70
#> precision 0.0068 0.0068
#>
#> V27
#> mean 0.6888 0.67
#> std. dev. 0.2836 0.2184
#> weight sum 69 70
#> precision 0.0074 0.0074
#>
#> V28
#> mean 0.6908 0.6653
#> std. dev. 0.2725 0.2107
#> weight sum 69 70
#> precision 0.0074 0.0074
#>
#> V29
#> mean 0.632 0.6326
#> std. dev. 0.2562 0.2383
#> weight sum 69 70
#> precision 0.0075 0.0075
#>
#> V3
#> mean 0.0498 0.0345
#> std. dev. 0.0385 0.0246
#> weight sum 69 70
#> precision 0.0016 0.0016
#>
#> V30
#> mean 0.5657 0.5844
#> std. dev. 0.2057 0.2452
#> weight sum 69 70
#> precision 0.0069 0.0069
#>
#> V31
#> mean 0.4759 0.5398
#> std. dev. 0.2117 0.215
#> weight sum 69 70
#> precision 0.0066 0.0066
#>
#> V32
#> mean 0.4294 0.4679
#> std. dev. 0.2121 0.2191
#> weight sum 69 70
#> precision 0.0065 0.0065
#>
#> V33
#> mean 0.4056 0.4554
#> std. dev. 0.1936 0.214
#> weight sum 69 70
#> precision 0.007 0.007
#>
#> V34
#> mean 0.3629 0.4674
#> std. dev. 0.2106 0.2614
#> weight sum 69 70
#> precision 0.0069 0.0069
#>
#> V35
#> mean 0.32 0.4822
#> std. dev. 0.2391 0.2819
#> weight sum 69 70
#> precision 0.0072 0.0072
#>
#> V36
#> mean 0.2925 0.4878
#> std. dev. 0.226 0.2722
#> weight sum 69 70
#> precision 0.0073 0.0073
#>
#> V37
#> mean 0.2799 0.4324
#> std. dev. 0.2126 0.2496
#> weight sum 69 70
#> precision 0.0067 0.0067
#>
#> V38
#> mean 0.3109 0.3577
#> std. dev. 0.1868 0.2256
#> weight sum 69 70
#> precision 0.007 0.007
#>
#> V39
#> mean 0.3187 0.323
#> std. dev. 0.1809 0.1994
#> weight sum 69 70
#> precision 0.0062 0.0062
#>
#> V4
#> mean 0.0657 0.0403
#> std. dev. 0.0445 0.0305
#> weight sum 69 70
#> precision 0.002 0.002
#>
#> V40
#> mean 0.2897 0.3154
#> std. dev. 0.1707 0.1769
#> weight sum 69 70
#> precision 0.0064 0.0064
#>
#> V41
#> mean 0.2837 0.2817
#> std. dev. 0.1771 0.1563
#> weight sum 69 70
#> precision 0.0054 0.0054
#>
#> V42
#> mean 0.2945 0.2505
#> std. dev. 0.1724 0.148
#> weight sum 69 70
#> precision 0.0059 0.0059
#>
#> V43
#> mean 0.271 0.2149
#> std. dev. 0.1356 0.1222
#> weight sum 69 70
#> precision 0.0057 0.0057
#>
#> V44
#> mean 0.2497 0.1854
#> std. dev. 0.1403 0.1113
#> weight sum 69 70
#> precision 0.0058 0.0058
#>
#> V45
#> mean 0.2375 0.1449
#> std. dev. 0.1654 0.0877
#> weight sum 69 70
#> precision 0.0048 0.0048
#>
#> V46
#> mean 0.1903 0.1134
#> std. dev. 0.1514 0.0772
#> weight sum 69 70
#> precision 0.0054 0.0054
#>
#> V47
#> mean 0.1442 0.0896
#> std. dev. 0.0988 0.0514
#> weight sum 69 70
#> precision 0.0041 0.0041
#>
#> V48
#> mean 0.1121 0.0659
#> std. dev. 0.0695 0.04
#> weight sum 69 70
#> precision 0.0024 0.0024
#>
#> V49
#> mean 0.0629 0.0377
#> std. dev. 0.035 0.0236
#> weight sum 69 70
#> precision 0.0012 0.0012
#>
#> V5
#> mean 0.0916 0.0625
#> std. dev. 0.0564 0.0467
#> weight sum 69 70
#> precision 0.0024 0.0024
#>
#> V50
#> mean 0.0218 0.0175
#> std. dev. 0.0129 0.0113
#> weight sum 69 70
#> precision 0.0008 0.0008
#>
#> V51
#> mean 0.0193 0.0118
#> std. dev. 0.0142 0.0075
#> weight sum 69 70
#> precision 0.0009 0.0009
#>
#> V52
#> mean 0.0171 0.0097
#> std. dev. 0.0115 0.0062
#> weight sum 69 70
#> precision 0.0007 0.0007
#>
#> V53
#> mean 0.0119 0.0097
#> std. dev. 0.0086 0.0058
#> weight sum 69 70
#> precision 0.0004 0.0004
#>
#> V54
#> mean 0.0122 0.0097
#> std. dev. 0.0088 0.0052
#> weight sum 69 70
#> precision 0.0003 0.0003
#>
#> V55
#> mean 0.011 0.0087
#> std. dev. 0.0094 0.0047
#> weight sum 69 70
#> precision 0.0004 0.0004
#>
#> V56
#> mean 0.0091 0.0072
#> std. dev. 0.0071 0.0049
#> weight sum 69 70
#> precision 0.0004 0.0004
#>
#> V57
#> mean 0.0084 0.0085
#> std. dev. 0.0067 0.0063
#> weight sum 69 70
#> precision 0.0004 0.0004
#>
#> V58
#> mean 0.0092 0.007
#> std. dev. 0.0075 0.005
#> weight sum 69 70
#> precision 0.0005 0.0005
#>
#> V59
#> mean 0.0092 0.0073
#> std. dev. 0.0074 0.0054
#> weight sum 69 70
#> precision 0.0004 0.0004
#>
#> V6
#> mean 0.1149 0.0959
#> std. dev. 0.0559 0.063
#> weight sum 69 70
#> precision 0.0022 0.0022
#>
#> V60
#> mean 0.0068 0.006
#> std. dev. 0.0061 0.0038
#> weight sum 69 70
#> precision 0.0005 0.0005
#>
#> V7
#> mean 0.1314 0.1126
#> std. dev. 0.0593 0.0621
#> weight sum 69 70
#> precision 0.0025 0.0025
#>
#> V8
#> mean 0.1485 0.1119
#> std. dev. 0.0825 0.0821
#> weight sum 69 70
#> precision 0.0034 0.0034
#>
#> V9
#> mean 0.2074 0.1325
#> std. dev. 0.114 0.1084
#> weight sum 69 70
#> precision 0.0047 0.0047
#>
#>
# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)
# Score the predictions
predictions$score()
#> classif.ce
#> 0.3913043