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
#> (0.53) (0.47)
#> ===============================
#> V1
#> mean 0.0335 0.0235
#> std. dev. 0.0239 0.0145
#> weight sum 74 65
#> precision 0.0011 0.0011
#>
#> V10
#> mean 0.2319 0.1688
#> std. dev. 0.1233 0.1193
#> weight sum 74 65
#> precision 0.0043 0.0043
#>
#> V11
#> mean 0.2804 0.1861
#> std. dev. 0.1163 0.126
#> weight sum 74 65
#> precision 0.0047 0.0047
#>
#> V12
#> mean 0.3018 0.2107
#> std. dev. 0.1195 0.1453
#> weight sum 74 65
#> precision 0.005 0.005
#>
#> V13
#> mean 0.315 0.2397
#> std. dev. 0.1236 0.1355
#> weight sum 74 65
#> precision 0.0049 0.0049
#>
#> V14
#> mean 0.3122 0.2707
#> std. dev. 0.1626 0.1676
#> weight sum 74 65
#> precision 0.0071 0.0071
#>
#> V15
#> mean 0.3088 0.301
#> std. dev. 0.1891 0.2134
#> weight sum 74 65
#> precision 0.0074 0.0074
#>
#> V16
#> mean 0.3518 0.3726
#> std. dev. 0.2029 0.248
#> weight sum 74 65
#> precision 0.0071 0.0071
#>
#> V17
#> mean 0.3948 0.4365
#> std. dev. 0.2282 0.2865
#> weight sum 74 65
#> precision 0.0071 0.0071
#>
#> V18
#> mean 0.436 0.4716
#> std. dev. 0.2438 0.2745
#> weight sum 74 65
#> precision 0.007 0.007
#>
#> V19
#> mean 0.5226 0.4763
#> std. dev. 0.2506 0.2715
#> weight sum 74 65
#> precision 0.0069 0.0069
#>
#> V2
#> mean 0.0462 0.0307
#> std. dev. 0.0338 0.0244
#> weight sum 74 65
#> precision 0.0013 0.0013
#>
#> V20
#> mean 0.597 0.5104
#> std. dev. 0.2468 0.2673
#> weight sum 74 65
#> precision 0.0069 0.0069
#>
#> V21
#> mean 0.6513 0.5511
#> std. dev. 0.2421 0.2499
#> weight sum 74 65
#> precision 0.0071 0.0071
#>
#> V22
#> mean 0.682 0.5694
#> std. dev. 0.2271 0.2656
#> weight sum 74 65
#> precision 0.0072 0.0072
#>
#> V23
#> mean 0.6997 0.6194
#> std. dev. 0.224 0.24
#> weight sum 74 65
#> precision 0.0071 0.0071
#>
#> V24
#> mean 0.7183 0.6533
#> std. dev. 0.2194 0.2399
#> weight sum 74 65
#> precision 0.0065 0.0065
#>
#> V25
#> mean 0.7106 0.6541
#> std. dev. 0.2268 0.2611
#> weight sum 74 65
#> precision 0.0075 0.0075
#>
#> V26
#> mean 0.7416 0.6659
#> std. dev. 0.2186 0.2546
#> weight sum 74 65
#> precision 0.0066 0.0066
#>
#> V27
#> mean 0.7608 0.6812
#> std. dev. 0.2379 0.2408
#> weight sum 74 65
#> precision 0.0073 0.0073
#>
#> V28
#> mean 0.7513 0.6758
#> std. dev. 0.2426 0.2229
#> weight sum 74 65
#> precision 0.0077 0.0077
#>
#> V29
#> mean 0.6822 0.6304
#> std. dev. 0.2318 0.2287
#> weight sum 74 65
#> precision 0.0075 0.0075
#>
#> V3
#> mean 0.051 0.038
#> std. dev. 0.0396 0.03
#> weight sum 74 65
#> precision 0.0015 0.0015
#>
#> V30
#> mean 0.591 0.5684
#> std. dev. 0.2063 0.2255
#> weight sum 74 65
#> precision 0.007 0.007
#>
#> V31
#> mean 0.4863 0.5158
#> std. dev. 0.2243 0.2002
#> weight sum 74 65
#> precision 0.0066 0.0066
#>
#> V32
#> mean 0.4259 0.4351
#> std. dev. 0.2122 0.2057
#> weight sum 74 65
#> precision 0.0065 0.0065
#>
#> V33
#> mean 0.3793 0.4218
#> std. dev. 0.1784 0.2175
#> weight sum 74 65
#> precision 0.0056 0.0056
#>
#> V34
#> mean 0.3433 0.4354
#> std. dev. 0.1762 0.2607
#> weight sum 74 65
#> precision 0.0067 0.0067
#>
#> V35
#> mean 0.3157 0.4519
#> std. dev. 0.2316 0.2611
#> weight sum 74 65
#> precision 0.0072 0.0072
#>
#> V36
#> mean 0.3017 0.4605
#> std. dev. 0.2384 0.2595
#> weight sum 74 65
#> precision 0.0072 0.0072
#>
#> V37
#> mean 0.3081 0.4189
#> std. dev. 0.221 0.2443
#> weight sum 74 65
#> precision 0.0066 0.0066
#>
#> V38
#> mean 0.3215 0.3513
#> std. dev. 0.1915 0.2177
#> weight sum 74 65
#> precision 0.007 0.007
#>
#> V39
#> mean 0.3263 0.2948
#> std. dev. 0.168 0.2054
#> weight sum 74 65
#> precision 0.0068 0.0068
#>
#> V4
#> mean 0.0615 0.0442
#> std. dev. 0.0451 0.033
#> weight sum 74 65
#> precision 0.002 0.002
#>
#> V40
#> mean 0.2944 0.3035
#> std. dev. 0.1472 0.1888
#> weight sum 74 65
#> precision 0.0067 0.0067
#>
#> V41
#> mean 0.2976 0.285
#> std. dev. 0.1594 0.1861
#> weight sum 74 65
#> precision 0.0063 0.0063
#>
#> V42
#> mean 0.3131 0.2562
#> std. dev. 0.1808 0.1724
#> weight sum 74 65
#> precision 0.0059 0.0059
#>
#> V43
#> mean 0.2862 0.2021
#> std. dev. 0.1431 0.1233
#> weight sum 74 65
#> precision 0.0053 0.0053
#>
#> V44
#> mean 0.2477 0.1588
#> std. dev. 0.1492 0.0803
#> weight sum 74 65
#> precision 0.0041 0.0041
#>
#> V45
#> mean 0.2402 0.1274
#> std. dev. 0.1749 0.0752
#> weight sum 74 65
#> precision 0.0047 0.0047
#>
#> V46
#> mean 0.1978 0.1064
#> std. dev. 0.1552 0.0866
#> weight sum 74 65
#> precision 0.0055 0.0055
#>
#> V47
#> mean 0.1458 0.0915
#> std. dev. 0.1024 0.0653
#> weight sum 74 65
#> precision 0.004 0.004
#>
#> V48
#> mean 0.1079 0.07
#> std. dev. 0.0704 0.0472
#> weight sum 74 65
#> precision 0.0024 0.0024
#>
#> V49
#> mean 0.0625 0.0386
#> std. dev. 0.0373 0.0317
#> weight sum 74 65
#> precision 0.0015 0.0015
#>
#> V5
#> mean 0.0857 0.0612
#> std. dev. 0.0539 0.0479
#> weight sum 74 65
#> precision 0.0024 0.0024
#>
#> V50
#> mean 0.0219 0.0178
#> std. dev. 0.0128 0.0136
#> weight sum 74 65
#> precision 0.0007 0.0007
#>
#> V51
#> mean 0.0207 0.0125
#> std. dev. 0.0154 0.0092
#> weight sum 74 65
#> precision 0.0009 0.0009
#>
#> V52
#> mean 0.0171 0.0102
#> std. dev. 0.0119 0.0078
#> weight sum 74 65
#> precision 0.0007 0.0007
#>
#> V53
#> mean 0.0114 0.0094
#> std. dev. 0.007 0.0058
#> weight sum 74 65
#> precision 0.0003 0.0003
#>
#> V54
#> mean 0.0122 0.0089
#> std. dev. 0.0086 0.0048
#> weight sum 74 65
#> precision 0.0003 0.0003
#>
#> V55
#> mean 0.0097 0.0086
#> std. dev. 0.0082 0.0048
#> weight sum 74 65
#> precision 0.0004 0.0004
#>
#> V56
#> mean 0.0088 0.0071
#> std. dev. 0.0055 0.0046
#> weight sum 74 65
#> precision 0.0003 0.0003
#>
#> V57
#> mean 0.0075 0.0077
#> std. dev. 0.0049 0.0054
#> weight sum 74 65
#> precision 0.0003 0.0003
#>
#> V58
#> mean 0.0083 0.0068
#> std. dev. 0.0062 0.0048
#> weight sum 74 65
#> precision 0.0003 0.0003
#>
#> V59
#> mean 0.0089 0.0072
#> std. dev. 0.0066 0.005
#> weight sum 74 65
#> precision 0.0004 0.0004
#>
#> V6
#> mean 0.1124 0.0952
#> std. dev. 0.055 0.059
#> weight sum 74 65
#> precision 0.0022 0.0022
#>
#> V60
#> mean 0.0066 0.006
#> std. dev. 0.0045 0.0035
#> weight sum 74 65
#> precision 0.0003 0.0003
#>
#> V7
#> mean 0.1229 0.1136
#> std. dev. 0.0573 0.0636
#> weight sum 74 65
#> precision 0.0022 0.0022
#>
#> V8
#> mean 0.1498 0.1211
#> std. dev. 0.085 0.0804
#> weight sum 74 65
#> precision 0.0033 0.0033
#>
#> V9
#> mean 0.2014 0.1451
#> std. dev. 0.1165 0.1058
#> weight sum 74 65
#> 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
#> 0.2173913