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
#> (0.52) (0.48)
#> ===============================
#> V1
#> mean 0.0361 0.0239
#> std. dev. 0.0263 0.0142
#> weight sum 73 66
#> precision 0.001 0.001
#>
#> V10
#> mean 0.2573 0.1632
#> std. dev. 0.1465 0.1154
#> weight sum 73 66
#> precision 0.0051 0.0051
#>
#> V11
#> mean 0.2909 0.1834
#> std. dev. 0.134 0.1195
#> weight sum 73 66
#> precision 0.0052 0.0052
#>
#> V12
#> mean 0.2942 0.1975
#> std. dev. 0.1268 0.1471
#> weight sum 73 66
#> precision 0.005 0.005
#>
#> V13
#> mean 0.3147 0.228
#> std. dev. 0.1261 0.1466
#> weight sum 73 66
#> precision 0.0049 0.0049
#>
#> V14
#> mean 0.3248 0.2737
#> std. dev. 0.1621 0.1773
#> weight sum 73 66
#> precision 0.0071 0.0071
#>
#> V15
#> mean 0.3291 0.3073
#> std. dev. 0.1936 0.2337
#> weight sum 73 66
#> precision 0.0073 0.0073
#>
#> V16
#> mean 0.3736 0.3695
#> std. dev. 0.2106 0.2689
#> weight sum 73 66
#> precision 0.0072 0.0072
#>
#> V17
#> mean 0.41 0.4161
#> std. dev. 0.2436 0.2889
#> weight sum 73 66
#> precision 0.0071 0.0071
#>
#> V18
#> mean 0.4474 0.4448
#> std. dev. 0.2583 0.265
#> weight sum 73 66
#> precision 0.0068 0.0068
#>
#> V19
#> mean 0.5314 0.4656
#> std. dev. 0.2533 0.2507
#> weight sum 73 66
#> precision 0.0069 0.0069
#>
#> V2
#> mean 0.0506 0.0322
#> std. dev. 0.0401 0.0261
#> weight sum 73 66
#> precision 0.0019 0.0019
#>
#> V20
#> mean 0.6095 0.5008
#> std. dev. 0.2552 0.239
#> weight sum 73 66
#> precision 0.0065 0.0065
#>
#> V21
#> mean 0.6632 0.5474
#> std. dev. 0.2533 0.2377
#> weight sum 73 66
#> precision 0.0072 0.0072
#>
#> V22
#> mean 0.6696 0.6008
#> std. dev. 0.2489 0.2566
#> weight sum 73 66
#> precision 0.0073 0.0073
#>
#> V23
#> mean 0.6628 0.6527
#> std. dev. 0.2631 0.2411
#> weight sum 73 66
#> precision 0.0071 0.0071
#>
#> V24
#> mean 0.6666 0.6795
#> std. dev. 0.2497 0.2316
#> weight sum 73 66
#> precision 0.0074 0.0074
#>
#> V25
#> mean 0.6623 0.695
#> std. dev. 0.2304 0.2389
#> weight sum 73 66
#> precision 0.0074 0.0074
#>
#> V26
#> mean 0.6766 0.709
#> std. dev. 0.2304 0.2345
#> weight sum 73 66
#> precision 0.0069 0.0069
#>
#> V27
#> mean 0.6819 0.6977
#> std. dev. 0.2641 0.2184
#> weight sum 73 66
#> precision 0.0075 0.0075
#>
#> V28
#> mean 0.6906 0.6745
#> std. dev. 0.2623 0.2008
#> weight sum 73 66
#> precision 0.0072 0.0072
#>
#> V29
#> mean 0.6398 0.6397
#> std. dev. 0.2505 0.2195
#> weight sum 73 66
#> precision 0.0072 0.0072
#>
#> V3
#> mean 0.0563 0.0369
#> std. dev. 0.0488 0.0315
#> weight sum 73 66
#> precision 0.0023 0.0023
#>
#> V30
#> mean 0.577 0.6012
#> std. dev. 0.229 0.2227
#> weight sum 73 66
#> precision 0.007 0.007
#>
#> V31
#> mean 0.4821 0.5487
#> std. dev. 0.2368 0.1977
#> weight sum 73 66
#> precision 0.0063 0.0063
#>
#> V32
#> mean 0.425 0.4518
#> std. dev. 0.2188 0.2127
#> weight sum 73 66
#> precision 0.0063 0.0063
#>
#> V33
#> mean 0.4018 0.4487
#> std. dev. 0.1898 0.2197
#> weight sum 73 66
#> precision 0.007 0.007
#>
#> V34
#> mean 0.39 0.4526
#> std. dev. 0.1969 0.2674
#> weight sum 73 66
#> precision 0.0069 0.0069
#>
#> V35
#> mean 0.375 0.4677
#> std. dev. 0.2448 0.2746
#> weight sum 73 66
#> precision 0.0071 0.0071
#>
#> V36
#> mean 0.351 0.4795
#> std. dev. 0.2552 0.266
#> weight sum 73 66
#> precision 0.0071 0.0071
#>
#> V37
#> mean 0.339 0.428
#> std. dev. 0.2366 0.2345
#> weight sum 73 66
#> precision 0.0067 0.0067
#>
#> V38
#> mean 0.3491 0.339
#> std. dev. 0.2079 0.2067
#> weight sum 73 66
#> precision 0.007 0.007
#>
#> V39
#> mean 0.3543 0.302
#> std. dev. 0.1871 0.1997
#> weight sum 73 66
#> precision 0.0062 0.0062
#>
#> V4
#> mean 0.0698 0.0414
#> std. dev. 0.0617 0.0339
#> weight sum 73 66
#> precision 0.0034 0.0034
#>
#> V40
#> mean 0.307 0.3256
#> std. dev. 0.169 0.1751
#> weight sum 73 66
#> precision 0.0064 0.0064
#>
#> V41
#> mean 0.3064 0.2924
#> std. dev. 0.1701 0.1679
#> weight sum 73 66
#> precision 0.0054 0.0054
#>
#> V42
#> mean 0.3197 0.2581
#> std. dev. 0.1793 0.1636
#> weight sum 73 66
#> precision 0.0059 0.0059
#>
#> V43
#> mean 0.29 0.2187
#> std. dev. 0.1473 0.1386
#> weight sum 73 66
#> precision 0.0054 0.0054
#>
#> V44
#> mean 0.2542 0.1872
#> std. dev. 0.1457 0.1165
#> weight sum 73 66
#> precision 0.0056 0.0056
#>
#> V45
#> mean 0.2627 0.1475
#> std. dev. 0.1797 0.0928
#> weight sum 73 66
#> precision 0.0052 0.0052
#>
#> V46
#> mean 0.2212 0.1152
#> std. dev. 0.1679 0.083
#> weight sum 73 66
#> precision 0.0053 0.0053
#>
#> V47
#> mean 0.1578 0.0974
#> std. dev. 0.1059 0.0587
#> weight sum 73 66
#> precision 0.004 0.004
#>
#> V48
#> mean 0.1169 0.0719
#> std. dev. 0.0734 0.0444
#> weight sum 73 66
#> precision 0.0024 0.0024
#>
#> V49
#> mean 0.0678 0.0384
#> std. dev. 0.0389 0.0274
#> weight sum 73 66
#> precision 0.0013 0.0013
#>
#> V5
#> mean 0.09 0.0652
#> std. dev. 0.0665 0.0501
#> weight sum 73 66
#> precision 0.0031 0.0031
#>
#> V50
#> mean 0.0245 0.0171
#> std. dev. 0.0156 0.0107
#> weight sum 73 66
#> precision 0.0007 0.0007
#>
#> V51
#> mean 0.0209 0.0122
#> std. dev. 0.0153 0.0082
#> weight sum 73 66
#> precision 0.0008 0.0008
#>
#> V52
#> mean 0.0171 0.0104
#> std. dev. 0.0123 0.0072
#> weight sum 73 66
#> precision 0.0006 0.0006
#>
#> V53
#> mean 0.0125 0.0101
#> std. dev. 0.0084 0.0064
#> weight sum 73 66
#> precision 0.0004 0.0004
#>
#> V54
#> mean 0.0128 0.0092
#> std. dev. 0.0087 0.0051
#> weight sum 73 66
#> precision 0.0003 0.0003
#>
#> V55
#> mean 0.0104 0.0079
#> std. dev. 0.0085 0.0052
#> weight sum 73 66
#> precision 0.0004 0.0004
#>
#> V56
#> mean 0.0093 0.0078
#> std. dev. 0.0068 0.0051
#> weight sum 73 66
#> precision 0.0004 0.0004
#>
#> V57
#> mean 0.0086 0.0083
#> std. dev. 0.0063 0.006
#> weight sum 73 66
#> precision 0.0004 0.0004
#>
#> V58
#> mean 0.0101 0.0069
#> std. dev. 0.0081 0.0048
#> weight sum 73 66
#> precision 0.0004 0.0004
#>
#> V59
#> mean 0.0096 0.007
#> std. dev. 0.0075 0.0048
#> weight sum 73 66
#> precision 0.0004 0.0004
#>
#> V6
#> mean 0.1151 0.1019
#> std. dev. 0.0599 0.0707
#> weight sum 73 66
#> precision 0.0028 0.0028
#>
#> V60
#> mean 0.0075 0.0061
#> std. dev. 0.0069 0.0039
#> weight sum 73 66
#> precision 0.0005 0.0005
#>
#> V7
#> mean 0.128 0.1232
#> std. dev. 0.0618 0.068
#> weight sum 73 66
#> precision 0.0027 0.0027
#>
#> V8
#> mean 0.1493 0.1303
#> std. dev. 0.088 0.0852
#> weight sum 73 66
#> precision 0.0033 0.0033
#>
#> V9
#> mean 0.218 0.1466
#> std. dev. 0.1288 0.1051
#> 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
#> 0.3478261