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
#> (0.58) (0.42)
#> ===============================
#> V1
#> mean 0.0369 0.0229
#> std. dev. 0.0284 0.0132
#> weight sum 81 58
#> precision 0.0011 0.0011
#>
#> V10
#> mean 0.2589 0.1595
#> std. dev. 0.1405 0.1194
#> weight sum 81 58
#> precision 0.0051 0.0051
#>
#> V11
#> mean 0.2923 0.171
#> std. dev. 0.1289 0.1118
#> weight sum 81 58
#> precision 0.0052 0.0052
#>
#> V12
#> mean 0.2957 0.183
#> std. dev. 0.123 0.1156
#> weight sum 81 58
#> precision 0.0039 0.0039
#>
#> V13
#> mean 0.316 0.2173
#> std. dev. 0.124 0.1272
#> weight sum 81 58
#> precision 0.0052 0.0052
#>
#> V14
#> mean 0.3244 0.2686
#> std. dev. 0.1616 0.1715
#> weight sum 81 58
#> precision 0.0071 0.0071
#>
#> V15
#> mean 0.3373 0.3006
#> std. dev. 0.1984 0.2259
#> weight sum 81 58
#> precision 0.0072 0.0072
#>
#> V16
#> mean 0.3883 0.3647
#> std. dev. 0.2218 0.25
#> weight sum 81 58
#> precision 0.007 0.007
#>
#> V17
#> mean 0.4215 0.4138
#> std. dev. 0.2452 0.2806
#> weight sum 81 58
#> precision 0.0069 0.0069
#>
#> V18
#> mean 0.4575 0.4447
#> std. dev. 0.2572 0.2671
#> weight sum 81 58
#> precision 0.0071 0.0071
#>
#> V19
#> mean 0.5395 0.4551
#> std. dev. 0.2545 0.247
#> weight sum 81 58
#> precision 0.0066 0.0066
#>
#> V2
#> mean 0.0466 0.0336
#> std. dev. 0.0385 0.0271
#> weight sum 81 58
#> precision 0.0018 0.0018
#>
#> V20
#> mean 0.6044 0.4905
#> std. dev. 0.2588 0.249
#> weight sum 81 58
#> precision 0.0067 0.0067
#>
#> V21
#> mean 0.6496 0.5346
#> std. dev. 0.2594 0.247
#> weight sum 81 58
#> precision 0.0072 0.0072
#>
#> V22
#> mean 0.6517 0.5471
#> std. dev. 0.2406 0.2617
#> weight sum 81 58
#> precision 0.0072 0.0072
#>
#> V23
#> mean 0.6637 0.5937
#> std. dev. 0.2557 0.2525
#> weight sum 81 58
#> precision 0.0072 0.0072
#>
#> V24
#> mean 0.6806 0.6491
#> std. dev. 0.2442 0.2356
#> weight sum 81 58
#> precision 0.0073 0.0073
#>
#> V25
#> mean 0.6677 0.6417
#> std. dev. 0.2385 0.2653
#> weight sum 81 58
#> precision 0.0072 0.0072
#>
#> V26
#> mean 0.6836 0.6584
#> std. dev. 0.2413 0.2388
#> weight sum 81 58
#> precision 0.0069 0.0069
#>
#> V27
#> mean 0.6936 0.6616
#> std. dev. 0.267 0.2138
#> weight sum 81 58
#> precision 0.0074 0.0074
#>
#> V28
#> mean 0.7064 0.6529
#> std. dev. 0.2662 0.2078
#> weight sum 81 58
#> precision 0.0075 0.0075
#>
#> V29
#> mean 0.6573 0.6155
#> std. dev. 0.2575 0.2514
#> weight sum 81 58
#> precision 0.0075 0.0075
#>
#> V3
#> mean 0.0533 0.0374
#> std. dev. 0.0476 0.0313
#> weight sum 81 58
#> precision 0.0024 0.0024
#>
#> V30
#> mean 0.5957 0.596
#> std. dev. 0.218 0.2366
#> weight sum 81 58
#> precision 0.0071 0.0071
#>
#> V31
#> mean 0.4957 0.5611
#> std. dev. 0.2311 0.2015
#> weight sum 81 58
#> precision 0.0066 0.0066
#>
#> V32
#> mean 0.4423 0.4811
#> std. dev. 0.2172 0.2093
#> weight sum 81 58
#> precision 0.0065 0.0065
#>
#> V33
#> mean 0.413 0.4661
#> std. dev. 0.1981 0.2036
#> weight sum 81 58
#> precision 0.007 0.007
#>
#> V34
#> mean 0.3845 0.4661
#> std. dev. 0.2117 0.2498
#> weight sum 81 58
#> precision 0.0068 0.0068
#>
#> V35
#> mean 0.3593 0.4653
#> std. dev. 0.2411 0.273
#> weight sum 81 58
#> precision 0.0072 0.0072
#>
#> V36
#> mean 0.3335 0.4893
#> std. dev. 0.2445 0.2626
#> weight sum 81 58
#> precision 0.0073 0.0073
#>
#> V37
#> mean 0.326 0.4465
#> std. dev. 0.228 0.2392
#> weight sum 81 58
#> precision 0.0067 0.0067
#>
#> V38
#> mean 0.3395 0.3781
#> std. dev. 0.2065 0.1955
#> weight sum 81 58
#> precision 0.0066 0.0066
#>
#> V39
#> mean 0.3414 0.3366
#> std. dev. 0.1859 0.1982
#> weight sum 81 58
#> precision 0.0062 0.0062
#>
#> V4
#> mean 0.0678 0.0448
#> std. dev. 0.059 0.0363
#> weight sum 81 58
#> precision 0.0033 0.0033
#>
#> V40
#> mean 0.3102 0.3349
#> std. dev. 0.1649 0.182
#> weight sum 81 58
#> precision 0.0063 0.0063
#>
#> V41
#> mean 0.3064 0.2909
#> std. dev. 0.1653 0.1726
#> weight sum 81 58
#> precision 0.0054 0.0054
#>
#> V42
#> mean 0.3097 0.2543
#> std. dev. 0.1708 0.1616
#> weight sum 81 58
#> precision 0.0057 0.0057
#>
#> V43
#> mean 0.2806 0.207
#> std. dev. 0.1356 0.1356
#> weight sum 81 58
#> precision 0.0056 0.0056
#>
#> V44
#> mean 0.2511 0.1772
#> std. dev. 0.1407 0.1123
#> weight sum 81 58
#> precision 0.0061 0.0061
#>
#> V45
#> mean 0.244 0.1407
#> std. dev. 0.1732 0.0992
#> weight sum 81 58
#> precision 0.0047 0.0047
#>
#> V46
#> mean 0.2009 0.115
#> std. dev. 0.1559 0.0821
#> weight sum 81 58
#> precision 0.0054 0.0054
#>
#> V47
#> mean 0.152 0.0902
#> std. dev. 0.0968 0.0569
#> weight sum 81 58
#> precision 0.0041 0.0041
#>
#> V48
#> mean 0.1158 0.0663
#> std. dev. 0.0699 0.0434
#> weight sum 81 58
#> precision 0.0024 0.0024
#>
#> V49
#> mean 0.0661 0.0381
#> std. dev. 0.0359 0.0259
#> weight sum 81 58
#> precision 0.0014 0.0014
#>
#> V5
#> mean 0.0899 0.0673
#> std. dev. 0.0638 0.0535
#> weight sum 81 58
#> precision 0.003 0.003
#>
#> V50
#> mean 0.023 0.0174
#> std. dev. 0.0145 0.0115
#> weight sum 81 58
#> precision 0.0007 0.0007
#>
#> V51
#> mean 0.0207 0.0117
#> std. dev. 0.0148 0.0083
#> weight sum 81 58
#> precision 0.0009 0.0009
#>
#> V52
#> mean 0.0167 0.0101
#> std. dev. 0.0114 0.0072
#> weight sum 81 58
#> precision 0.0006 0.0006
#>
#> V53
#> mean 0.0121 0.01
#> std. dev. 0.008 0.0064
#> weight sum 81 58
#> precision 0.0004 0.0004
#>
#> V54
#> mean 0.013 0.0099
#> std. dev. 0.009 0.0055
#> weight sum 81 58
#> precision 0.0003 0.0003
#>
#> V55
#> mean 0.0104 0.0089
#> std. dev. 0.0086 0.0055
#> weight sum 81 58
#> precision 0.0004 0.0004
#>
#> V56
#> mean 0.0093 0.0067
#> std. dev. 0.0065 0.004
#> weight sum 81 58
#> precision 0.0004 0.0004
#>
#> V57
#> mean 0.0084 0.0073
#> std. dev. 0.0063 0.0045
#> weight sum 81 58
#> precision 0.0004 0.0004
#>
#> V58
#> mean 0.0095 0.0064
#> std. dev. 0.0073 0.005
#> weight sum 81 58
#> precision 0.0005 0.0005
#>
#> V59
#> mean 0.0091 0.0078
#> std. dev. 0.0073 0.0059
#> weight sum 81 58
#> precision 0.0004 0.0004
#>
#> V6
#> mean 0.1113 0.0978
#> std. dev. 0.0551 0.0716
#> weight sum 81 58
#> precision 0.0028 0.0028
#>
#> V60
#> mean 0.007 0.006
#> std. dev. 0.0064 0.0035
#> weight sum 81 58
#> precision 0.0005 0.0005
#>
#> V7
#> mean 0.1302 0.1142
#> std. dev. 0.063 0.071
#> weight sum 81 58
#> precision 0.0028 0.0028
#>
#> V8
#> mean 0.1497 0.1149
#> std. dev. 0.0904 0.0791
#> weight sum 81 58
#> precision 0.0033 0.0033
#>
#> V9
#> mean 0.2158 0.1389
#> std. dev. 0.1232 0.0963
#> weight sum 81 58
#> 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
#> 0.2318841