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
#> (0.53) (0.47)
#> ===============================
#> V1
#> mean 0.0366 0.0223
#> std. dev. 0.0284 0.0136
#> weight sum 74 65
#> precision 0.0011 0.0011
#>
#> V10
#> mean 0.258 0.1507
#> std. dev. 0.1379 0.1045
#> weight sum 74 65
#> precision 0.0047 0.0047
#>
#> V11
#> mean 0.2856 0.164
#> std. dev. 0.1131 0.107
#> weight sum 74 65
#> precision 0.0044 0.0044
#>
#> V12
#> mean 0.2901 0.188
#> std. dev. 0.1165 0.1369
#> weight sum 74 65
#> precision 0.005 0.005
#>
#> V13
#> mean 0.3032 0.2236
#> std. dev. 0.1248 0.1299
#> weight sum 74 65
#> precision 0.0051 0.0051
#>
#> V14
#> mean 0.3125 0.2689
#> std. dev. 0.1623 0.1587
#> weight sum 74 65
#> precision 0.0071 0.0071
#>
#> V15
#> mean 0.3296 0.3095
#> std. dev. 0.2025 0.2185
#> weight sum 74 65
#> precision 0.0073 0.0073
#>
#> V16
#> mean 0.3796 0.3792
#> std. dev. 0.2139 0.2517
#> weight sum 74 65
#> precision 0.0072 0.0072
#>
#> V17
#> mean 0.4077 0.4168
#> std. dev. 0.2341 0.2839
#> weight sum 74 65
#> precision 0.0071 0.0071
#>
#> V18
#> mean 0.4509 0.4505
#> std. dev. 0.2567 0.2611
#> weight sum 74 65
#> precision 0.0066 0.0066
#>
#> V19
#> mean 0.5394 0.4771
#> std. dev. 0.2615 0.2612
#> weight sum 74 65
#> precision 0.0069 0.0069
#>
#> V2
#> mean 0.0475 0.0318
#> std. dev. 0.0409 0.0259
#> weight sum 74 65
#> precision 0.0018 0.0018
#>
#> V20
#> mean 0.6295 0.5023
#> std. dev. 0.2672 0.2749
#> weight sum 74 65
#> precision 0.0069 0.0069
#>
#> V21
#> mean 0.6854 0.5465
#> std. dev. 0.2613 0.2589
#> weight sum 74 65
#> precision 0.0072 0.0072
#>
#> V22
#> mean 0.6887 0.5677
#> std. dev. 0.2387 0.2663
#> weight sum 74 65
#> precision 0.0073 0.0073
#>
#> V23
#> mean 0.6918 0.6094
#> std. dev. 0.2522 0.2482
#> weight sum 74 65
#> precision 0.007 0.007
#>
#> V24
#> mean 0.6884 0.6627
#> std. dev. 0.2458 0.235
#> weight sum 74 65
#> precision 0.0074 0.0074
#>
#> V25
#> mean 0.6678 0.6889
#> std. dev. 0.2534 0.2496
#> weight sum 74 65
#> precision 0.0074 0.0074
#>
#> V26
#> mean 0.6843 0.7104
#> std. dev. 0.2394 0.2381
#> weight sum 74 65
#> precision 0.0065 0.0065
#>
#> V27
#> mean 0.6886 0.683
#> std. dev. 0.2667 0.2306
#> weight sum 74 65
#> precision 0.0069 0.0069
#>
#> V28
#> mean 0.6953 0.6511
#> std. dev. 0.2629 0.1955
#> weight sum 74 65
#> precision 0.0075 0.0075
#>
#> V29
#> mean 0.6417 0.5909
#> std. dev. 0.2427 0.2233
#> weight sum 74 65
#> precision 0.0073 0.0073
#>
#> V3
#> mean 0.0512 0.0362
#> std. dev. 0.0474 0.0314
#> weight sum 74 65
#> precision 0.0024 0.0024
#>
#> V30
#> mean 0.5786 0.5412
#> std. dev. 0.2061 0.2334
#> weight sum 74 65
#> precision 0.007 0.007
#>
#> V31
#> mean 0.4734 0.5109
#> std. dev. 0.2225 0.1951
#> weight sum 74 65
#> precision 0.0063 0.0063
#>
#> V32
#> mean 0.4198 0.4407
#> std. dev. 0.2159 0.2112
#> weight sum 74 65
#> precision 0.0064 0.0064
#>
#> V33
#> mean 0.3948 0.4225
#> std. dev. 0.208 0.2195
#> weight sum 74 65
#> precision 0.007 0.007
#>
#> V34
#> mean 0.3813 0.4211
#> std. dev. 0.2217 0.2538
#> weight sum 74 65
#> precision 0.0067 0.0067
#>
#> V35
#> mean 0.3748 0.4412
#> std. dev. 0.2654 0.2628
#> weight sum 74 65
#> precision 0.0072 0.0072
#>
#> V36
#> mean 0.3662 0.4506
#> std. dev. 0.2665 0.2657
#> weight sum 74 65
#> precision 0.0073 0.0073
#>
#> V37
#> mean 0.3496 0.4166
#> std. dev. 0.248 0.2484
#> weight sum 74 65
#> precision 0.0066 0.0066
#>
#> V38
#> mean 0.3521 0.3338
#> std. dev. 0.2243 0.2244
#> weight sum 74 65
#> precision 0.0066 0.0066
#>
#> V39
#> mean 0.3438 0.3087
#> std. dev. 0.1904 0.2269
#> weight sum 74 65
#> precision 0.007 0.007
#>
#> V4
#> mean 0.0691 0.0419
#> std. dev. 0.0601 0.0302
#> weight sum 74 65
#> precision 0.0033 0.0033
#>
#> V40
#> mean 0.3146 0.3326
#> std. dev. 0.1666 0.2093
#> weight sum 74 65
#> precision 0.0067 0.0067
#>
#> V41
#> mean 0.3046 0.3069
#> std. dev. 0.1684 0.1797
#> weight sum 74 65
#> precision 0.0062 0.0062
#>
#> V42
#> mean 0.3024 0.2772
#> std. dev. 0.1693 0.1679
#> weight sum 74 65
#> precision 0.0058 0.0058
#>
#> V43
#> mean 0.2762 0.2246
#> std. dev. 0.1436 0.1351
#> weight sum 74 65
#> precision 0.0055 0.0055
#>
#> V44
#> mean 0.2489 0.1823
#> std. dev. 0.1406 0.1122
#> weight sum 74 65
#> precision 0.0055 0.0055
#>
#> V45
#> mean 0.2493 0.1544
#> std. dev. 0.1748 0.1043
#> weight sum 74 65
#> precision 0.0051 0.0051
#>
#> V46
#> mean 0.2088 0.128
#> std. dev. 0.1519 0.1006
#> weight sum 74 65
#> precision 0.0054 0.0054
#>
#> V47
#> mean 0.1521 0.1014
#> std. dev. 0.1009 0.0715
#> weight sum 74 65
#> precision 0.0039 0.0039
#>
#> V48
#> mean 0.1135 0.0759
#> std. dev. 0.0698 0.0507
#> weight sum 74 65
#> precision 0.0024 0.0024
#>
#> V49
#> mean 0.0667 0.0433
#> std. dev. 0.0374 0.0336
#> weight sum 74 65
#> precision 0.0015 0.0015
#>
#> V5
#> mean 0.0903 0.059
#> std. dev. 0.063 0.0412
#> weight sum 74 65
#> precision 0.003 0.003
#>
#> V50
#> mean 0.0241 0.018
#> std. dev. 0.0139 0.0135
#> weight sum 74 65
#> precision 0.0007 0.0007
#>
#> V51
#> mean 0.0194 0.012
#> std. dev. 0.0155 0.0081
#> weight sum 74 65
#> precision 0.0009 0.0009
#>
#> V52
#> mean 0.0168 0.0102
#> std. dev. 0.0118 0.0063
#> weight sum 74 65
#> precision 0.0007 0.0007
#>
#> V53
#> mean 0.0111 0.0095
#> std. dev. 0.0076 0.0058
#> weight sum 74 65
#> precision 0.0004 0.0004
#>
#> V54
#> mean 0.0121 0.0095
#> std. dev. 0.009 0.0051
#> weight sum 74 65
#> precision 0.0003 0.0003
#>
#> V55
#> mean 0.0097 0.0089
#> std. dev. 0.0082 0.0056
#> weight sum 74 65
#> precision 0.0004 0.0004
#>
#> V56
#> mean 0.0093 0.0077
#> std. dev. 0.0058 0.005
#> weight sum 74 65
#> precision 0.0003 0.0003
#>
#> V57
#> mean 0.0076 0.0082
#> std. dev. 0.0049 0.0061
#> weight sum 74 65
#> precision 0.0003 0.0003
#>
#> V58
#> mean 0.0095 0.0068
#> std. dev. 0.0064 0.0048
#> weight sum 74 65
#> precision 0.0003 0.0003
#>
#> V59
#> mean 0.009 0.0065
#> std. dev. 0.0074 0.0043
#> weight sum 74 65
#> precision 0.0004 0.0004
#>
#> V6
#> mean 0.1194 0.0887
#> std. dev. 0.0563 0.0533
#> weight sum 74 65
#> precision 0.002 0.002
#>
#> V60
#> mean 0.0072 0.0059
#> std. dev. 0.0063 0.0036
#> weight sum 74 65
#> precision 0.0005 0.0005
#>
#> V7
#> mean 0.1339 0.1073
#> std. dev. 0.0615 0.0516
#> weight sum 74 65
#> precision 0.0025 0.0025
#>
#> V8
#> mean 0.1602 0.1102
#> std. dev. 0.0985 0.0747
#> weight sum 74 65
#> precision 0.0034 0.0034
#>
#> V9
#> mean 0.2243 0.1236
#> std. dev. 0.1379 0.0893
#> weight sum 74 65
#> 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
#> 0.3913043