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 0.0405 0.0218
#> std. dev. 0.0305 0.016
#> weight sum 70 69
#> precision 0.0011 0.0011
#>
#> V10
#> mean 0.2556 0.159
#> std. dev. 0.1467 0.1173
#> weight sum 70 69
#> precision 0.0051 0.0051
#>
#> V11
#> mean 0.2988 0.1729
#> std. dev. 0.1298 0.1188
#> weight sum 70 69
#> precision 0.0052 0.0052
#>
#> V12
#> mean 0.3079 0.1908
#> std. dev. 0.1261 0.1371
#> weight sum 70 69
#> precision 0.0049 0.0049
#>
#> V13
#> mean 0.3177 0.2193
#> std. dev. 0.1397 0.1443
#> weight sum 70 69
#> precision 0.0052 0.0052
#>
#> V14
#> mean 0.3287 0.262
#> std. dev. 0.1607 0.1669
#> weight sum 70 69
#> precision 0.0059 0.0059
#>
#> V15
#> mean 0.3562 0.2998
#> std. dev. 0.1999 0.2078
#> weight sum 70 69
#> precision 0.0073 0.0073
#>
#> V16
#> mean 0.4083 0.3585
#> std. dev. 0.2199 0.2341
#> weight sum 70 69
#> precision 0.0068 0.0068
#>
#> V17
#> mean 0.4274 0.3844
#> std. dev. 0.252 0.2641
#> weight sum 70 69
#> precision 0.0071 0.0071
#>
#> V18
#> mean 0.4524 0.4086
#> std. dev. 0.2663 0.2478
#> weight sum 70 69
#> precision 0.0067 0.0067
#>
#> V19
#> mean 0.5261 0.4407
#> std. dev. 0.2586 0.2449
#> weight sum 70 69
#> precision 0.0069 0.0069
#>
#> V2
#> mean 0.054 0.0277
#> std. dev. 0.0418 0.0194
#> weight sum 70 69
#> precision 0.0018 0.0018
#>
#> V20
#> mean 0.6207 0.4841
#> std. dev. 0.2526 0.2608
#> weight sum 70 69
#> precision 0.007 0.007
#>
#> V21
#> mean 0.6784 0.5122
#> std. dev. 0.2425 0.2515
#> weight sum 70 69
#> precision 0.0072 0.0072
#>
#> V22
#> mean 0.6616 0.5269
#> std. dev. 0.2402 0.2639
#> weight sum 70 69
#> precision 0.0072 0.0072
#>
#> V23
#> mean 0.647 0.5983
#> std. dev. 0.2699 0.2428
#> weight sum 70 69
#> precision 0.007 0.007
#>
#> V24
#> mean 0.6618 0.6432
#> std. dev. 0.2672 0.234
#> weight sum 70 69
#> precision 0.0072 0.0072
#>
#> V25
#> mean 0.6541 0.6496
#> std. dev. 0.2623 0.2628
#> weight sum 70 69
#> precision 0.0073 0.0073
#>
#> V26
#> mean 0.6845 0.6797
#> std. dev. 0.2458 0.2513
#> weight sum 70 69
#> precision 0.0065 0.0065
#>
#> V27
#> mean 0.6933 0.6859
#> std. dev. 0.2741 0.2333
#> weight sum 70 69
#> precision 0.0072 0.0072
#>
#> V28
#> mean 0.6993 0.6855
#> std. dev. 0.2584 0.2141
#> weight sum 70 69
#> precision 0.0076 0.0076
#>
#> V29
#> mean 0.6338 0.64
#> std. dev. 0.245 0.2379
#> weight sum 70 69
#> precision 0.0075 0.0075
#>
#> V3
#> mean 0.0559 0.0331
#> std. dev. 0.0466 0.0281
#> weight sum 70 69
#> precision 0.0023 0.0023
#>
#> V30
#> mean 0.5796 0.5775
#> std. dev. 0.2143 0.236
#> weight sum 70 69
#> precision 0.007 0.007
#>
#> V31
#> mean 0.4775 0.5262
#> std. dev. 0.2148 0.2057
#> weight sum 70 69
#> precision 0.0063 0.0063
#>
#> V32
#> mean 0.4086 0.4523
#> std. dev. 0.2053 0.2061
#> weight sum 70 69
#> precision 0.0062 0.0062
#>
#> V33
#> mean 0.3753 0.4431
#> std. dev. 0.1864 0.2005
#> weight sum 70 69
#> precision 0.0063 0.0063
#>
#> V34
#> mean 0.3667 0.4483
#> std. dev. 0.2144 0.2497
#> weight sum 70 69
#> precision 0.0067 0.0067
#>
#> V35
#> mean 0.3577 0.4561
#> std. dev. 0.2609 0.2616
#> weight sum 70 69
#> precision 0.0071 0.0071
#>
#> V36
#> mean 0.3238 0.4734
#> std. dev. 0.261 0.2526
#> weight sum 70 69
#> precision 0.0071 0.0071
#>
#> V37
#> mean 0.321 0.4296
#> std. dev. 0.2335 0.2429
#> weight sum 70 69
#> precision 0.0066 0.0066
#>
#> V38
#> mean 0.3366 0.3535
#> std. dev. 0.2215 0.229
#> weight sum 70 69
#> precision 0.007 0.007
#>
#> V39
#> mean 0.332 0.3213
#> std. dev. 0.1898 0.2134
#> weight sum 70 69
#> precision 0.007 0.007
#>
#> V4
#> mean 0.0692 0.0412
#> std. dev. 0.0593 0.0304
#> weight sum 70 69
#> precision 0.0033 0.0033
#>
#> V40
#> mean 0.2923 0.3158
#> std. dev. 0.1561 0.202
#> weight sum 70 69
#> precision 0.0067 0.0067
#>
#> V41
#> mean 0.2917 0.2804
#> std. dev. 0.1628 0.1766
#> weight sum 70 69
#> precision 0.0063 0.0063
#>
#> V42
#> mean 0.3024 0.2451
#> std. dev. 0.1718 0.1605
#> weight sum 70 69
#> precision 0.0058 0.0058
#>
#> V43
#> mean 0.2666 0.1984
#> std. dev. 0.1458 0.1097
#> weight sum 70 69
#> precision 0.0055 0.0055
#>
#> V44
#> mean 0.2346 0.1613
#> std. dev. 0.1377 0.084
#> weight sum 70 69
#> precision 0.0043 0.0043
#>
#> V45
#> mean 0.2391 0.1318
#> std. dev. 0.1691 0.0848
#> weight sum 70 69
#> precision 0.0047 0.0047
#>
#> V46
#> mean 0.1957 0.1126
#> std. dev. 0.1563 0.093
#> weight sum 70 69
#> precision 0.0056 0.0056
#>
#> V47
#> mean 0.1415 0.0913
#> std. dev. 0.0923 0.069
#> weight sum 70 69
#> precision 0.0041 0.0041
#>
#> V48
#> mean 0.1081 0.0684
#> std. dev. 0.0623 0.0487
#> weight sum 70 69
#> precision 0.0025 0.0025
#>
#> V49
#> mean 0.0627 0.0395
#> std. dev. 0.0343 0.0324
#> weight sum 70 69
#> precision 0.0015 0.0015
#>
#> V5
#> mean 0.0843 0.0586
#> std. dev. 0.0609 0.043
#> weight sum 70 69
#> precision 0.003 0.003
#>
#> V50
#> mean 0.0229 0.018
#> std. dev. 0.014 0.0134
#> weight sum 70 69
#> precision 0.0007 0.0007
#>
#> V51
#> mean 0.0202 0.0128
#> std. dev. 0.0153 0.0094
#> weight sum 70 69
#> precision 0.0009 0.0009
#>
#> V52
#> mean 0.0171 0.0107
#> std. dev. 0.0115 0.0075
#> weight sum 70 69
#> precision 0.0007 0.0007
#>
#> V53
#> mean 0.0123 0.0092
#> std. dev. 0.0074 0.0056
#> weight sum 70 69
#> precision 0.0004 0.0004
#>
#> V54
#> mean 0.0134 0.0091
#> std. dev. 0.0088 0.0053
#> weight sum 70 69
#> precision 0.0003 0.0003
#>
#> V55
#> mean 0.0103 0.0083
#> std. dev. 0.0078 0.0048
#> weight sum 70 69
#> precision 0.0004 0.0004
#>
#> V56
#> mean 0.0094 0.0072
#> std. dev. 0.0058 0.0047
#> weight sum 70 69
#> precision 0.0004 0.0004
#>
#> V57
#> mean 0.008 0.0085
#> std. dev. 0.0055 0.0058
#> weight sum 70 69
#> precision 0.0003 0.0003
#>
#> V58
#> mean 0.0095 0.0069
#> std. dev. 0.0072 0.005
#> weight sum 70 69
#> precision 0.0004 0.0004
#>
#> V59
#> mean 0.0095 0.007
#> std. dev. 0.0071 0.005
#> weight sum 70 69
#> precision 0.0004 0.0004
#>
#> V6
#> mean 0.1163 0.0867
#> std. dev. 0.0518 0.0518
#> weight sum 70 69
#> precision 0.0018 0.0018
#>
#> V60
#> mean 0.008 0.0054
#> std. dev. 0.0067 0.0031
#> weight sum 70 69
#> precision 0.0005 0.0005
#>
#> V7
#> mean 0.1332 0.1047
#> std. dev. 0.0563 0.0576
#> weight sum 70 69
#> precision 0.0025 0.0025
#>
#> V8
#> mean 0.1553 0.1144
#> std. dev. 0.0969 0.0829
#> weight sum 70 69
#> precision 0.0034 0.0034
#>
#> V9
#> mean 0.222 0.1372
#> std. dev. 0.1329 0.1011
#> weight sum 70 69
#> 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
#> 0.4492754