Classification Naive Bayes Learner From Weka
Source:R/learner_RWeka_classif_naive_bayes_weka.R
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/chapters/chapter2/data_and_basic_modeling.html#sec-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
Methods
Inherited methods
mlr3::Learner$base_learner()mlr3::Learner$configure()mlr3::Learner$encapsulate()mlr3::Learner$format()mlr3::Learner$help()mlr3::Learner$predict()mlr3::Learner$predict_newdata()mlr3::Learner$print()mlr3::Learner$reset()mlr3::Learner$selected_features()mlr3::Learner$train()mlr3::LearnerClassif$predict_newdata_fast()
LearnerClassifNaiveBayesWeka$marshal()
Marshal the learner's model.
Arguments
...(any)
Additional arguments passed tomlr3::marshal_model().
LearnerClassifNaiveBayesWeka$unmarshal()
Unmarshal the learner's model.
Arguments
...(any)
Additional arguments passed tomlr3::unmarshal_model().
Examples
# Define the Learner
learner = lrn("classif.naive_bayes_weka")
print(learner)
#>
#> ── <LearnerClassifNaiveBayesWeka> (classif.naive_bayes_weka): Naive Bayes ──────
#> • Model: -
#> • Parameters: list()
#> • Packages: mlr3 and RWeka
#> • Predict Types: [response] and prob
#> • Feature Types: logical, integer, numeric, factor, and ordered
#> • Encapsulation: none (fallback: -)
#> • Properties: marshal, missings, multiclass, and twoclass
#> • Other settings: use_weights = 'error', predict_raw = 'FALSE'
# Define a Task
task = tsk("sonar")
# Create train and test set
ids = 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
#> (0.52) (0.48)
#> ===============================
#> V1
#> mean 0.0364 0.023
#> std. dev. 0.0283 0.016
#> weight sum 73 66
#> precision 0.0011 0.0011
#>
#> V10
#> mean 0.2514 0.1664
#> std. dev. 0.129 0.1215
#> weight sum 73 66
#> precision 0.0047 0.0047
#>
#> V11
#> mean 0.2768 0.1837
#> std. dev. 0.1154 0.1267
#> weight sum 73 66
#> precision 0.0044 0.0044
#>
#> V12
#> mean 0.2857 0.1994
#> std. dev. 0.1251 0.1453
#> weight sum 73 66
#> precision 0.0049 0.0049
#>
#> V13
#> mean 0.3051 0.225
#> std. dev. 0.1331 0.1425
#> weight sum 73 66
#> precision 0.0052 0.0052
#>
#> V14
#> mean 0.3093 0.269
#> std. dev. 0.1739 0.1693
#> weight sum 73 66
#> precision 0.0072 0.0072
#>
#> V15
#> mean 0.3081 0.3058
#> std. dev. 0.1994 0.2215
#> weight sum 73 66
#> precision 0.0073 0.0073
#>
#> V16
#> mean 0.3629 0.3748
#> std. dev. 0.2141 0.2553
#> weight sum 73 66
#> precision 0.0072 0.0072
#>
#> V17
#> mean 0.3892 0.4241
#> std. dev. 0.235 0.2894
#> weight sum 73 66
#> precision 0.0071 0.0071
#>
#> V18
#> mean 0.4269 0.4527
#> std. dev. 0.2485 0.2558
#> weight sum 73 66
#> precision 0.0071 0.0071
#>
#> V19
#> mean 0.5038 0.4781
#> std. dev. 0.2507 0.2425
#> weight sum 73 66
#> precision 0.0067 0.0067
#>
#> V2
#> mean 0.0458 0.0305
#> std. dev. 0.0396 0.0244
#> weight sum 73 66
#> precision 0.0019 0.0019
#>
#> V20
#> mean 0.5864 0.5111
#> std. dev. 0.2585 0.2629
#> weight sum 73 66
#> precision 0.0069 0.0069
#>
#> V21
#> mean 0.6322 0.5452
#> std. dev. 0.2546 0.2519
#> weight sum 73 66
#> precision 0.0071 0.0071
#>
#> V22
#> mean 0.6379 0.5416
#> std. dev. 0.2345 0.26
#> weight sum 73 66
#> precision 0.0071 0.0071
#>
#> V23
#> mean 0.657 0.5816
#> std. dev. 0.2534 0.2358
#> weight sum 73 66
#> precision 0.007 0.007
#>
#> V24
#> mean 0.665 0.6374
#> std. dev. 0.2474 0.2397
#> weight sum 73 66
#> precision 0.0073 0.0073
#>
#> V25
#> mean 0.6663 0.6557
#> std. dev. 0.2465 0.2656
#> weight sum 73 66
#> precision 0.0072 0.0072
#>
#> V26
#> mean 0.7108 0.6778
#> std. dev. 0.2355 0.2549
#> weight sum 73 66
#> precision 0.0065 0.0065
#>
#> V27
#> mean 0.7288 0.679
#> std. dev. 0.2612 0.2369
#> weight sum 73 66
#> precision 0.0071 0.0071
#>
#> V28
#> mean 0.7444 0.665
#> std. dev. 0.2378 0.217
#> weight sum 73 66
#> precision 0.0077 0.0077
#>
#> V29
#> mean 0.6845 0.6252
#> std. dev. 0.2197 0.2299
#> weight sum 73 66
#> precision 0.007 0.007
#>
#> V3
#> mean 0.0493 0.0378
#> std. dev. 0.0451 0.03
#> weight sum 73 66
#> precision 0.0024 0.0024
#>
#> V30
#> mean 0.6169 0.573
#> std. dev. 0.1989 0.234
#> weight sum 73 66
#> precision 0.0068 0.0068
#>
#> V31
#> mean 0.5046 0.5221
#> std. dev. 0.2256 0.201
#> weight sum 73 66
#> precision 0.0067 0.0067
#>
#> V32
#> mean 0.4489 0.4454
#> std. dev. 0.2163 0.2117
#> weight sum 73 66
#> precision 0.0063 0.0063
#>
#> V33
#> mean 0.4098 0.4418
#> std. dev. 0.1987 0.2154
#> weight sum 73 66
#> precision 0.007 0.007
#>
#> V34
#> mean 0.3695 0.4368
#> std. dev. 0.2058 0.2639
#> weight sum 73 66
#> precision 0.0068 0.0068
#>
#> V35
#> mean 0.3414 0.4485
#> std. dev. 0.2582 0.2621
#> weight sum 73 66
#> precision 0.0072 0.0072
#>
#> V36
#> mean 0.3247 0.4694
#> std. dev. 0.2729 0.2599
#> weight sum 73 66
#> precision 0.0071 0.0071
#>
#> V37
#> mean 0.3345 0.4453
#> std. dev. 0.2492 0.2257
#> weight sum 73 66
#> precision 0.0066 0.0066
#>
#> V38
#> mean 0.3466 0.3637
#> std. dev. 0.2219 0.2196
#> weight sum 73 66
#> precision 0.0071 0.0071
#>
#> V39
#> mean 0.3432 0.3274
#> std. dev. 0.1938 0.2345
#> weight sum 73 66
#> precision 0.007 0.007
#>
#> V4
#> mean 0.0642 0.0419
#> std. dev. 0.0574 0.0326
#> weight sum 73 66
#> precision 0.0033 0.0033
#>
#> V40
#> mean 0.3136 0.3281
#> std. dev. 0.1707 0.2095
#> weight sum 73 66
#> precision 0.0067 0.0067
#>
#> V41
#> mean 0.3134 0.2888
#> std. dev. 0.1764 0.1767
#> weight sum 73 66
#> precision 0.0064 0.0064
#>
#> V42
#> mean 0.3106 0.2602
#> std. dev. 0.1697 0.1612
#> weight sum 73 66
#> precision 0.0058 0.0058
#>
#> V43
#> mean 0.2826 0.2096
#> std. dev. 0.1398 0.131
#> weight sum 73 66
#> precision 0.0056 0.0056
#>
#> V44
#> mean 0.2601 0.1814
#> std. dev. 0.1467 0.116
#> weight sum 73 66
#> precision 0.0061 0.0061
#>
#> V45
#> mean 0.2619 0.1473
#> std. dev. 0.1764 0.1049
#> weight sum 73 66
#> precision 0.0051 0.0051
#>
#> V46
#> mean 0.2156 0.1181
#> std. dev. 0.1494 0.099
#> weight sum 73 66
#> precision 0.0046 0.0046
#>
#> V47
#> mean 0.1549 0.0967
#> std. dev. 0.0947 0.0736
#> weight sum 73 66
#> precision 0.0032 0.0032
#>
#> V48
#> mean 0.1143 0.0742
#> std. dev. 0.0686 0.0546
#> weight sum 73 66
#> precision 0.0022 0.0022
#>
#> V49
#> mean 0.0659 0.0418
#> std. dev. 0.0372 0.0345
#> weight sum 73 66
#> precision 0.0015 0.0015
#>
#> V5
#> mean 0.0867 0.061
#> std. dev. 0.06 0.0473
#> weight sum 73 66
#> precision 0.0029 0.0029
#>
#> V50
#> mean 0.0242 0.0172
#> std. dev. 0.0153 0.0124
#> weight sum 73 66
#> precision 0.0007 0.0007
#>
#> V51
#> mean 0.019 0.0118
#> std. dev. 0.0123 0.0077
#> weight sum 73 66
#> precision 0.0007 0.0007
#>
#> V52
#> mean 0.0161 0.0094
#> std. dev. 0.0105 0.0052
#> weight sum 73 66
#> precision 0.0004 0.0004
#>
#> V53
#> mean 0.0118 0.0092
#> std. dev. 0.0079 0.0055
#> weight sum 73 66
#> precision 0.0004 0.0004
#>
#> V54
#> mean 0.0113 0.0099
#> std. dev. 0.0087 0.0053
#> weight sum 73 66
#> precision 0.0003 0.0003
#>
#> V55
#> mean 0.0099 0.009
#> std. dev. 0.009 0.0054
#> weight sum 73 66
#> precision 0.0004 0.0004
#>
#> V56
#> mean 0.009 0.0075
#> std. dev. 0.0069 0.005
#> weight sum 73 66
#> precision 0.0004 0.0004
#>
#> V57
#> mean 0.0075 0.0082
#> std. dev. 0.0057 0.006
#> weight sum 73 66
#> precision 0.0004 0.0004
#>
#> V58
#> mean 0.0093 0.0067
#> std. dev. 0.0079 0.005
#> weight sum 73 66
#> precision 0.0005 0.0005
#>
#> V59
#> mean 0.0089 0.0074
#> std. dev. 0.0074 0.0056
#> weight sum 73 66
#> precision 0.0004 0.0004
#>
#> V6
#> mean 0.114 0.0931
#> std. dev. 0.0527 0.0611
#> weight sum 73 66
#> precision 0.0022 0.0022
#>
#> V60
#> mean 0.0071 0.0057
#> std. dev. 0.0066 0.0037
#> weight sum 73 66
#> precision 0.0005 0.0005
#>
#> V7
#> mean 0.1309 0.1172
#> std. dev. 0.0608 0.0612
#> weight sum 73 66
#> precision 0.0024 0.0024
#>
#> V8
#> mean 0.1484 0.1224
#> std. dev. 0.0948 0.0853
#> weight sum 73 66
#> precision 0.0033 0.0033
#>
#> V9
#> mean 0.2183 0.1448
#> std. dev. 0.1307 0.1084
#> weight sum 73 66
#> 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
#> 0.2898551