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
#> (0.54) (0.46)
#> ===============================
#> V1
#> mean 0.0326 0.023
#> std. dev. 0.022 0.0162
#> weight sum 75 64
#> precision 0.0009 0.0009
#>
#> V10
#> mean 0.2599 0.1639
#> std. dev. 0.1469 0.1112
#> weight sum 75 64
#> precision 0.0051 0.0051
#>
#> V11
#> mean 0.2979 0.1775
#> std. dev. 0.1324 0.1078
#> weight sum 75 64
#> precision 0.0052 0.0052
#>
#> V12
#> mean 0.3054 0.1902
#> std. dev. 0.1257 0.1232
#> weight sum 75 64
#> precision 0.004 0.004
#>
#> V13
#> mean 0.3134 0.2168
#> std. dev. 0.1212 0.1254
#> weight sum 75 64
#> precision 0.0045 0.0045
#>
#> V14
#> mean 0.3197 0.2597
#> std. dev. 0.1587 0.1548
#> weight sum 75 64
#> precision 0.0071 0.0071
#>
#> V15
#> mean 0.3256 0.3049
#> std. dev. 0.2008 0.2172
#> weight sum 75 64
#> precision 0.0073 0.0073
#>
#> V16
#> mean 0.3779 0.3684
#> std. dev. 0.2117 0.2487
#> weight sum 75 64
#> precision 0.0072 0.0072
#>
#> V17
#> mean 0.4057 0.424
#> std. dev. 0.2324 0.2894
#> weight sum 75 64
#> precision 0.0069 0.0069
#>
#> V18
#> mean 0.458 0.4452
#> std. dev. 0.2444 0.2709
#> weight sum 75 64
#> precision 0.007 0.007
#>
#> V19
#> mean 0.5513 0.4453
#> std. dev. 0.2453 0.2506
#> weight sum 75 64
#> precision 0.0068 0.0068
#>
#> V2
#> mean 0.0437 0.0313
#> std. dev. 0.0323 0.0263
#> weight sum 75 64
#> precision 0.0013 0.0013
#>
#> V20
#> mean 0.6426 0.4739
#> std. dev. 0.2459 0.2471
#> weight sum 75 64
#> precision 0.0069 0.0069
#>
#> V21
#> mean 0.6925 0.5143
#> std. dev. 0.2353 0.2418
#> weight sum 75 64
#> precision 0.007 0.007
#>
#> V22
#> mean 0.7033 0.5559
#> std. dev. 0.2346 0.2714
#> weight sum 75 64
#> precision 0.0073 0.0073
#>
#> V23
#> mean 0.7085 0.5967
#> std. dev. 0.2424 0.2509
#> weight sum 75 64
#> precision 0.0069 0.0069
#>
#> V24
#> mean 0.715 0.6244
#> std. dev. 0.2331 0.2488
#> weight sum 75 64
#> precision 0.0073 0.0073
#>
#> V25
#> mean 0.7056 0.6412
#> std. dev. 0.2284 0.2666
#> weight sum 75 64
#> precision 0.0075 0.0075
#>
#> V26
#> mean 0.7219 0.6753
#> std. dev. 0.2236 0.2495
#> weight sum 75 64
#> precision 0.0065 0.0065
#>
#> V27
#> mean 0.7202 0.6803
#> std. dev. 0.264 0.2216
#> weight sum 75 64
#> precision 0.0073 0.0073
#>
#> V28
#> mean 0.7095 0.6649
#> std. dev. 0.2627 0.2035
#> weight sum 75 64
#> precision 0.0075 0.0075
#>
#> V29
#> mean 0.6454 0.6314
#> std. dev. 0.2464 0.2267
#> weight sum 75 64
#> precision 0.0074 0.0074
#>
#> V3
#> mean 0.0481 0.0374
#> std. dev. 0.0358 0.0305
#> weight sum 75 64
#> precision 0.0015 0.0015
#>
#> V30
#> mean 0.5727 0.5838
#> std. dev. 0.2089 0.2292
#> weight sum 75 64
#> precision 0.007 0.007
#>
#> V31
#> mean 0.47 0.5249
#> std. dev. 0.2129 0.1981
#> weight sum 75 64
#> precision 0.0063 0.0063
#>
#> V32
#> mean 0.4226 0.4475
#> std. dev. 0.2028 0.2205
#> weight sum 75 64
#> precision 0.0064 0.0064
#>
#> V33
#> mean 0.4064 0.459
#> std. dev. 0.1914 0.228
#> weight sum 75 64
#> precision 0.007 0.007
#>
#> V34
#> mean 0.3784 0.4643
#> std. dev. 0.2088 0.2774
#> weight sum 75 64
#> precision 0.0068 0.0068
#>
#> V35
#> mean 0.3372 0.4711
#> std. dev. 0.2539 0.2746
#> weight sum 75 64
#> precision 0.0072 0.0072
#>
#> V36
#> mean 0.334 0.4928
#> std. dev. 0.2589 0.2653
#> weight sum 75 64
#> precision 0.0072 0.0072
#>
#> V37
#> mean 0.3393 0.4545
#> std. dev. 0.2389 0.2487
#> weight sum 75 64
#> precision 0.0067 0.0067
#>
#> V38
#> mean 0.3458 0.3624
#> std. dev. 0.2113 0.2265
#> weight sum 75 64
#> precision 0.007 0.007
#>
#> V39
#> mean 0.3576 0.3057
#> std. dev. 0.1863 0.2117
#> weight sum 75 64
#> precision 0.0069 0.0069
#>
#> V4
#> mean 0.0625 0.0425
#> std. dev. 0.0441 0.0341
#> weight sum 75 64
#> precision 0.002 0.002
#>
#> V40
#> mean 0.3233 0.3211
#> std. dev. 0.1642 0.1899
#> weight sum 75 64
#> precision 0.0067 0.0067
#>
#> V41
#> mean 0.2928 0.3053
#> std. dev. 0.1563 0.1742
#> weight sum 75 64
#> precision 0.0063 0.0063
#>
#> V42
#> mean 0.2982 0.2712
#> std. dev. 0.1519 0.1659
#> weight sum 75 64
#> precision 0.0057 0.0057
#>
#> V43
#> mean 0.2747 0.2081
#> std. dev. 0.1333 0.1199
#> weight sum 75 64
#> precision 0.0045 0.0045
#>
#> V44
#> mean 0.2407 0.1615
#> std. dev. 0.1417 0.0815
#> weight sum 75 64
#> precision 0.0043 0.0043
#>
#> V45
#> mean 0.237 0.1345
#> std. dev. 0.1657 0.076
#> weight sum 75 64
#> precision 0.0052 0.0052
#>
#> V46
#> mean 0.1866 0.1155
#> std. dev. 0.1304 0.0916
#> weight sum 75 64
#> precision 0.0045 0.0045
#>
#> V47
#> mean 0.1367 0.0951
#> std. dev. 0.0784 0.07
#> weight sum 75 64
#> precision 0.0032 0.0032
#>
#> V48
#> mean 0.1091 0.0726
#> std. dev. 0.0653 0.0516
#> weight sum 75 64
#> precision 0.0022 0.0022
#>
#> V49
#> mean 0.0641 0.0405
#> std. dev. 0.0345 0.0337
#> weight sum 75 64
#> precision 0.0015 0.0015
#>
#> V5
#> mean 0.0869 0.0635
#> std. dev. 0.0552 0.0497
#> weight sum 75 64
#> precision 0.0025 0.0025
#>
#> V50
#> mean 0.022 0.0172
#> std. dev. 0.0139 0.0119
#> weight sum 75 64
#> precision 0.0007 0.0007
#>
#> V51
#> mean 0.0172 0.0129
#> std. dev. 0.0098 0.0092
#> weight sum 75 64
#> precision 0.0004 0.0004
#>
#> V52
#> mean 0.0149 0.0108
#> std. dev. 0.0087 0.0078
#> weight sum 75 64
#> precision 0.0004 0.0004
#>
#> V53
#> mean 0.011 0.0105
#> std. dev. 0.0078 0.0066
#> weight sum 75 64
#> precision 0.0004 0.0004
#>
#> V54
#> mean 0.0121 0.0097
#> std. dev. 0.0077 0.005
#> weight sum 75 64
#> precision 0.0003 0.0003
#>
#> V55
#> mean 0.0101 0.009
#> std. dev. 0.0086 0.005
#> weight sum 75 64
#> precision 0.0004 0.0004
#>
#> V56
#> mean 0.009 0.0069
#> std. dev. 0.0067 0.0039
#> weight sum 75 64
#> precision 0.0004 0.0004
#>
#> V57
#> mean 0.008 0.0074
#> std. dev. 0.006 0.0051
#> weight sum 75 64
#> precision 0.0004 0.0004
#>
#> V58
#> mean 0.0091 0.007
#> std. dev. 0.0075 0.0048
#> weight sum 75 64
#> precision 0.0005 0.0005
#>
#> V59
#> mean 0.0085 0.0074
#> std. dev. 0.0065 0.0052
#> weight sum 75 64
#> precision 0.0004 0.0004
#>
#> V6
#> mean 0.1111 0.0947
#> std. dev. 0.0548 0.0542
#> weight sum 75 64
#> precision 0.0023 0.0023
#>
#> V60
#> mean 0.0068 0.0064
#> std. dev. 0.0061 0.0037
#> weight sum 75 64
#> precision 0.0005 0.0005
#>
#> V7
#> mean 0.1276 0.1077
#> std. dev. 0.0611 0.0605
#> weight sum 75 64
#> precision 0.0024 0.0024
#>
#> V8
#> mean 0.1542 0.1108
#> std. dev. 0.0921 0.0755
#> weight sum 75 64
#> precision 0.0034 0.0034
#>
#> V9
#> mean 0.2177 0.1313
#> std. dev. 0.131 0.0931
#> weight sum 75 64
#> 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
#> 0.2028986