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 and RWeka
#> • Predict Types: [response] and prob
#> • Feature Types: logical, integer, numeric, factor, and ordered
#> • Encapsulation: none (fallback: -)
#> • Properties: missings, multiclass, and twoclass
#> • Other settings: use_weights = 'error'
# 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 0.0366 0.0228
#> std. dev. 0.0264 0.0156
#> weight sum 75 64
#> precision 0.0011 0.0011
#>
#> V10
#> mean 0.252 0.1677
#> std. dev. 0.1315 0.1257
#> weight sum 75 64
#> precision 0.005 0.005
#>
#> V11
#> mean 0.2907 0.1804
#> std. dev. 0.1308 0.1198
#> weight sum 75 64
#> precision 0.0051 0.0051
#>
#> V12
#> mean 0.3103 0.1915
#> std. dev. 0.1285 0.1349
#> weight sum 75 64
#> precision 0.005 0.005
#>
#> V13
#> mean 0.3301 0.2209
#> std. dev. 0.1348 0.1324
#> weight sum 75 64
#> precision 0.005 0.005
#>
#> V14
#> mean 0.338 0.2629
#> std. dev. 0.1663 0.1587
#> weight sum 75 64
#> precision 0.0073 0.0073
#>
#> V15
#> mean 0.3401 0.3081
#> std. dev. 0.1924 0.2167
#> weight sum 75 64
#> precision 0.0066 0.0066
#>
#> V16
#> mean 0.385 0.3769
#> std. dev. 0.2114 0.2519
#> weight sum 75 64
#> precision 0.0072 0.0072
#>
#> V17
#> mean 0.4211 0.4125
#> std. dev. 0.253 0.2845
#> weight sum 75 64
#> precision 0.0071 0.0071
#>
#> V18
#> mean 0.4553 0.4377
#> std. dev. 0.2695 0.2628
#> weight sum 75 64
#> precision 0.007 0.007
#>
#> V19
#> mean 0.534 0.455
#> std. dev. 0.2679 0.2506
#> weight sum 75 64
#> precision 0.0069 0.0069
#>
#> V2
#> mean 0.0501 0.031
#> std. dev. 0.0409 0.0261
#> weight sum 75 64
#> precision 0.0018 0.0018
#>
#> V20
#> mean 0.6064 0.5009
#> std. dev. 0.2615 0.2559
#> weight sum 75 64
#> precision 0.0068 0.0068
#>
#> V21
#> mean 0.6445 0.555
#> std. dev. 0.2658 0.2457
#> weight sum 75 64
#> precision 0.0071 0.0071
#>
#> V22
#> mean 0.6589 0.5938
#> std. dev. 0.2486 0.2585
#> weight sum 75 64
#> precision 0.0072 0.0072
#>
#> V23
#> mean 0.6632 0.6182
#> std. dev. 0.2408 0.2465
#> weight sum 75 64
#> precision 0.007 0.007
#>
#> V24
#> mean 0.674 0.6504
#> std. dev. 0.2374 0.2326
#> weight sum 75 64
#> precision 0.0073 0.0073
#>
#> V25
#> mean 0.6699 0.6668
#> std. dev. 0.2287 0.2438
#> weight sum 75 64
#> precision 0.0075 0.0075
#>
#> V26
#> mean 0.7023 0.6795
#> std. dev. 0.2287 0.2327
#> weight sum 75 64
#> precision 0.0071 0.0071
#>
#> V27
#> mean 0.7174 0.6753
#> std. dev. 0.2528 0.2126
#> weight sum 75 64
#> precision 0.0075 0.0075
#>
#> V28
#> mean 0.7061 0.679
#> std. dev. 0.2529 0.1919
#> weight sum 75 64
#> precision 0.0069 0.0069
#>
#> V29
#> mean 0.6399 0.6431
#> std. dev. 0.2562 0.2297
#> weight sum 75 64
#> precision 0.0074 0.0074
#>
#> V3
#> mean 0.058 0.035
#> std. dev. 0.05 0.0312
#> weight sum 75 64
#> precision 0.0023 0.0023
#>
#> V30
#> mean 0.5771 0.5916
#> std. dev. 0.2324 0.2344
#> weight sum 75 64
#> precision 0.0069 0.0069
#>
#> V31
#> mean 0.4996 0.5325
#> std. dev. 0.2401 0.1981
#> weight sum 75 64
#> precision 0.0067 0.0067
#>
#> V32
#> mean 0.4356 0.4376
#> std. dev. 0.2328 0.2172
#> weight sum 75 64
#> precision 0.0064 0.0064
#>
#> V33
#> mean 0.3969 0.4221
#> std. dev. 0.1899 0.2184
#> weight sum 75 64
#> precision 0.0067 0.0067
#>
#> V34
#> mean 0.3628 0.4506
#> std. dev. 0.1888 0.2702
#> weight sum 75 64
#> precision 0.0068 0.0068
#>
#> V35
#> mean 0.3307 0.4895
#> std. dev. 0.2319 0.2668
#> weight sum 75 64
#> precision 0.0072 0.0072
#>
#> V36
#> mean 0.3109 0.4911
#> std. dev. 0.2297 0.2574
#> weight sum 75 64
#> precision 0.0071 0.0071
#>
#> V37
#> mean 0.3116 0.4229
#> std. dev. 0.2096 0.2306
#> weight sum 75 64
#> precision 0.0066 0.0066
#>
#> V38
#> mean 0.3294 0.334
#> std. dev. 0.1814 0.2018
#> weight sum 75 64
#> precision 0.007 0.007
#>
#> V39
#> mean 0.3362 0.2935
#> std. dev. 0.1707 0.2035
#> weight sum 75 64
#> precision 0.0061 0.0061
#>
#> V4
#> mean 0.0737 0.0425
#> std. dev. 0.0606 0.0329
#> weight sum 75 64
#> precision 0.0034 0.0034
#>
#> V40
#> mean 0.2935 0.3191
#> std. dev. 0.1569 0.1863
#> weight sum 75 64
#> precision 0.0064 0.0064
#>
#> V41
#> mean 0.2966 0.291
#> std. dev. 0.1728 0.164
#> weight sum 75 64
#> precision 0.0054 0.0054
#>
#> V42
#> mean 0.3305 0.2479
#> std. dev. 0.1767 0.1523
#> weight sum 75 64
#> precision 0.0059 0.0059
#>
#> V43
#> mean 0.2995 0.2122
#> std. dev. 0.149 0.1238
#> weight sum 75 64
#> precision 0.0055 0.0055
#>
#> V44
#> mean 0.2618 0.1767
#> std. dev. 0.1581 0.1171
#> weight sum 75 64
#> precision 0.0056 0.0056
#>
#> V45
#> mean 0.2612 0.1417
#> std. dev. 0.1877 0.0912
#> weight sum 75 64
#> precision 0.0051 0.0051
#>
#> V46
#> mean 0.2122 0.1167
#> std. dev. 0.1697 0.0833
#> weight sum 75 64
#> precision 0.0054 0.0054
#>
#> V47
#> mean 0.1602 0.0978
#> std. dev. 0.1069 0.06
#> weight sum 75 64
#> precision 0.004 0.004
#>
#> V48
#> mean 0.1219 0.0719
#> std. dev. 0.0717 0.0451
#> weight sum 75 64
#> precision 0.0024 0.0024
#>
#> V49
#> mean 0.0677 0.0392
#> std. dev. 0.0392 0.029
#> weight sum 75 64
#> precision 0.0014 0.0014
#>
#> V5
#> mean 0.0962 0.0646
#> std. dev. 0.0656 0.0495
#> weight sum 75 64
#> precision 0.003 0.003
#>
#> V50
#> mean 0.0239 0.0188
#> std. dev. 0.015 0.0115
#> weight sum 75 64
#> precision 0.0007 0.0007
#>
#> V51
#> mean 0.0217 0.013
#> std. dev. 0.0152 0.0084
#> weight sum 75 64
#> precision 0.0009 0.0009
#>
#> V52
#> mean 0.0167 0.0108
#> std. dev. 0.0121 0.0073
#> weight sum 75 64
#> precision 0.0006 0.0006
#>
#> V53
#> mean 0.0122 0.0101
#> std. dev. 0.0079 0.0061
#> weight sum 75 64
#> precision 0.0004 0.0004
#>
#> V54
#> mean 0.013 0.0101
#> std. dev. 0.0084 0.0055
#> weight sum 75 64
#> precision 0.0003 0.0003
#>
#> V55
#> mean 0.0101 0.0085
#> std. dev. 0.0087 0.0049
#> weight sum 75 64
#> precision 0.0004 0.0004
#>
#> V56
#> mean 0.0093 0.0072
#> std. dev. 0.0067 0.0051
#> weight sum 75 64
#> precision 0.0004 0.0004
#>
#> V57
#> mean 0.0081 0.0081
#> std. dev. 0.0062 0.006
#> weight sum 75 64
#> precision 0.0004 0.0004
#>
#> V58
#> mean 0.0095 0.0069
#> std. dev. 0.0083 0.0048
#> weight sum 75 64
#> precision 0.0005 0.0005
#>
#> V59
#> mean 0.0093 0.0075
#> std. dev. 0.0075 0.0054
#> weight sum 75 64
#> precision 0.0004 0.0004
#>
#> V6
#> mean 0.116 0.0998
#> std. dev. 0.0539 0.0666
#> weight sum 75 64
#> precision 0.0028 0.0028
#>
#> V60
#> mean 0.0073 0.006
#> std. dev. 0.0067 0.0039
#> weight sum 75 64
#> precision 0.0005 0.0005
#>
#> V7
#> mean 0.1283 0.1173
#> std. dev. 0.0577 0.0655
#> weight sum 75 64
#> precision 0.0027 0.0027
#>
#> V8
#> mean 0.1505 0.1237
#> std. dev. 0.0807 0.0834
#> weight sum 75 64
#> precision 0.0033 0.0033
#>
#> V9
#> mean 0.2142 0.145
#> std. dev. 0.117 0.109
#> weight sum 75 64
#> 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
#> 0.3333333