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
#> (0.55) (0.45)
#> ===============================
#> V1
#> mean 0.0364 0.0233
#> std. dev. 0.0289 0.0156
#> weight sum 76 63
#> precision 0.0011 0.0011
#>
#> V10
#> mean 0.2532 0.159
#> std. dev. 0.1383 0.1156
#> weight sum 76 63
#> precision 0.0051 0.0051
#>
#> V11
#> mean 0.2855 0.1765
#> std. dev. 0.1213 0.1155
#> weight sum 76 63
#> precision 0.0052 0.0052
#>
#> V12
#> mean 0.2897 0.1907
#> std. dev. 0.1217 0.1264
#> weight sum 76 63
#> precision 0.0046 0.0046
#>
#> V13
#> mean 0.3014 0.2237
#> std. dev. 0.1281 0.1351
#> weight sum 76 63
#> precision 0.0053 0.0053
#>
#> V14
#> mean 0.3073 0.2758
#> std. dev. 0.148 0.1675
#> weight sum 76 63
#> precision 0.0056 0.0056
#>
#> V15
#> mean 0.3279 0.3119
#> std. dev. 0.1821 0.2178
#> weight sum 76 63
#> precision 0.0063 0.0063
#>
#> V16
#> mean 0.3783 0.3787
#> std. dev. 0.209 0.2553
#> weight sum 76 63
#> precision 0.0071 0.0071
#>
#> V17
#> mean 0.4142 0.4266
#> std. dev. 0.2321 0.2931
#> weight sum 76 63
#> precision 0.0072 0.0072
#>
#> V18
#> mean 0.4501 0.4607
#> std. dev. 0.252 0.2664
#> weight sum 76 63
#> precision 0.0071 0.0071
#>
#> V19
#> mean 0.5257 0.476
#> std. dev. 0.2588 0.2509
#> weight sum 76 63
#> precision 0.0066 0.0066
#>
#> V2
#> mean 0.0462 0.0303
#> std. dev. 0.0381 0.0249
#> weight sum 76 63
#> precision 0.0019 0.0019
#>
#> V20
#> mean 0.6033 0.5109
#> std. dev. 0.2652 0.2514
#> weight sum 76 63
#> precision 0.0068 0.0068
#>
#> V21
#> mean 0.6478 0.546
#> std. dev. 0.254 0.2367
#> weight sum 76 63
#> precision 0.0071 0.0071
#>
#> V22
#> mean 0.6457 0.5653
#> std. dev. 0.244 0.2608
#> weight sum 76 63
#> precision 0.0072 0.0072
#>
#> V23
#> mean 0.6539 0.6136
#> std. dev. 0.2557 0.2342
#> weight sum 76 63
#> precision 0.007 0.007
#>
#> V24
#> mean 0.6735 0.6661
#> std. dev. 0.2503 0.2293
#> weight sum 76 63
#> precision 0.0073 0.0073
#>
#> V25
#> mean 0.6669 0.6644
#> std. dev. 0.2559 0.2522
#> weight sum 76 63
#> precision 0.0073 0.0073
#>
#> V26
#> mean 0.6952 0.6648
#> std. dev. 0.2472 0.2354
#> weight sum 76 63
#> precision 0.0071 0.0071
#>
#> V27
#> mean 0.7096 0.6701
#> std. dev. 0.265 0.2112
#> weight sum 76 63
#> precision 0.0074 0.0074
#>
#> V28
#> mean 0.7125 0.6644
#> std. dev. 0.252 0.2086
#> weight sum 76 63
#> precision 0.0077 0.0077
#>
#> V29
#> mean 0.6528 0.632
#> std. dev. 0.2437 0.2358
#> weight sum 76 63
#> precision 0.0075 0.0075
#>
#> V3
#> mean 0.0537 0.0362
#> std. dev. 0.0468 0.0291
#> weight sum 76 63
#> precision 0.0024 0.0024
#>
#> V30
#> mean 0.5958 0.5849
#> std. dev. 0.2105 0.2359
#> weight sum 76 63
#> precision 0.007 0.007
#>
#> V31
#> mean 0.4991 0.5351
#> std. dev. 0.2226 0.2058
#> weight sum 76 63
#> precision 0.0067 0.0067
#>
#> V32
#> mean 0.4381 0.4475
#> std. dev. 0.204 0.2222
#> weight sum 76 63
#> precision 0.0065 0.0065
#>
#> V33
#> mean 0.4033 0.4511
#> std. dev. 0.1805 0.222
#> weight sum 76 63
#> precision 0.0068 0.0068
#>
#> V34
#> mean 0.3714 0.4494
#> std. dev. 0.2019 0.2626
#> weight sum 76 63
#> precision 0.0068 0.0068
#>
#> V35
#> mean 0.3478 0.4479
#> std. dev. 0.2378 0.2616
#> weight sum 76 63
#> precision 0.0072 0.0072
#>
#> V36
#> mean 0.31 0.4563
#> std. dev. 0.2482 0.2489
#> weight sum 76 63
#> precision 0.0073 0.0073
#>
#> V37
#> mean 0.3093 0.4124
#> std. dev. 0.2241 0.2243
#> weight sum 76 63
#> precision 0.0065 0.0065
#>
#> V38
#> mean 0.3271 0.3452
#> std. dev. 0.2096 0.1941
#> weight sum 76 63
#> precision 0.0066 0.0066
#>
#> V39
#> mean 0.32 0.3062
#> std. dev. 0.1818 0.1915
#> weight sum 76 63
#> precision 0.0069 0.0069
#>
#> V4
#> mean 0.069 0.0436
#> std. dev. 0.0594 0.0328
#> weight sum 76 63
#> precision 0.0032 0.0032
#>
#> V40
#> mean 0.2935 0.3156
#> std. dev. 0.1587 0.1781
#> weight sum 76 63
#> precision 0.0065 0.0065
#>
#> V41
#> mean 0.2951 0.2834
#> std. dev. 0.1633 0.1605
#> weight sum 76 63
#> precision 0.0051 0.0051
#>
#> V42
#> mean 0.2935 0.2503
#> std. dev. 0.1701 0.1548
#> weight sum 76 63
#> precision 0.0059 0.0059
#>
#> V43
#> mean 0.2629 0.2061
#> std. dev. 0.137 0.1149
#> weight sum 76 63
#> precision 0.0055 0.0055
#>
#> V44
#> mean 0.2394 0.168
#> std. dev. 0.1312 0.0808
#> weight sum 76 63
#> precision 0.0043 0.0043
#>
#> V45
#> mean 0.2355 0.139
#> std. dev. 0.1717 0.0822
#> weight sum 76 63
#> precision 0.0051 0.0051
#>
#> V46
#> mean 0.201 0.1128
#> std. dev. 0.1553 0.0812
#> weight sum 76 63
#> precision 0.0054 0.0054
#>
#> V47
#> mean 0.1456 0.0904
#> std. dev. 0.0941 0.0591
#> weight sum 76 63
#> precision 0.0041 0.0041
#>
#> V48
#> mean 0.1085 0.0669
#> std. dev. 0.0647 0.0444
#> weight sum 76 63
#> precision 0.0024 0.0024
#>
#> V49
#> mean 0.0608 0.0371
#> std. dev. 0.0322 0.0276
#> weight sum 76 63
#> precision 0.0012 0.0012
#>
#> V5
#> mean 0.0846 0.0623
#> std. dev. 0.0635 0.0517
#> weight sum 76 63
#> precision 0.003 0.003
#>
#> V50
#> mean 0.022 0.0171
#> std. dev. 0.0129 0.0112
#> weight sum 76 63
#> precision 0.0008 0.0008
#>
#> V51
#> mean 0.0188 0.0116
#> std. dev. 0.0131 0.0082
#> weight sum 76 63
#> precision 0.0009 0.0009
#>
#> V52
#> mean 0.0165 0.0106
#> std. dev. 0.0107 0.0069
#> weight sum 76 63
#> precision 0.0007 0.0007
#>
#> V53
#> mean 0.0123 0.0095
#> std. dev. 0.0079 0.0062
#> weight sum 76 63
#> precision 0.0004 0.0004
#>
#> V54
#> mean 0.013 0.0107
#> std. dev. 0.0088 0.0053
#> weight sum 76 63
#> precision 0.0003 0.0003
#>
#> V55
#> mean 0.0098 0.009
#> std. dev. 0.0084 0.0053
#> weight sum 76 63
#> precision 0.0005 0.0005
#>
#> V56
#> mean 0.009 0.0069
#> std. dev. 0.0067 0.004
#> weight sum 76 63
#> precision 0.0004 0.0004
#>
#> V57
#> mean 0.0078 0.0077
#> std. dev. 0.0062 0.0054
#> weight sum 76 63
#> precision 0.0004 0.0004
#>
#> V58
#> mean 0.0089 0.006
#> std. dev. 0.0072 0.0043
#> weight sum 76 63
#> precision 0.0005 0.0005
#>
#> V59
#> mean 0.0087 0.0071
#> std. dev. 0.0074 0.0048
#> weight sum 76 63
#> precision 0.0004 0.0004
#>
#> V6
#> mean 0.1075 0.0972
#> std. dev. 0.0482 0.0724
#> weight sum 76 63
#> precision 0.0028 0.0028
#>
#> V60
#> mean 0.007 0.0058
#> std. dev. 0.0065 0.0033
#> weight sum 76 63
#> precision 0.0005 0.0005
#>
#> V7
#> mean 0.1259 0.1176
#> std. dev. 0.0578 0.0701
#> weight sum 76 63
#> precision 0.0027 0.0027
#>
#> V8
#> mean 0.148 0.1205
#> std. dev. 0.0891 0.0835
#> weight sum 76 63
#> precision 0.0033 0.0033
#>
#> V9
#> mean 0.2174 0.1405
#> std. dev. 0.1235 0.0993
#> weight sum 76 63
#> 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
#> 0.2608696