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 0.0335 0.023
#> std. dev. 0.0245 0.0162
#> weight sum 73 66
#> precision 0.0011 0.0011
#>
#> V10
#> mean 0.2448 0.1706
#> std. dev. 0.1386 0.1181
#> weight sum 73 66
#> precision 0.0051 0.0051
#>
#> V11
#> mean 0.2927 0.187
#> std. dev. 0.1336 0.1168
#> weight sum 73 66
#> precision 0.005 0.005
#>
#> V12
#> mean 0.3117 0.2018
#> std. dev. 0.1257 0.1431
#> weight sum 73 66
#> precision 0.0049 0.0049
#>
#> V13
#> mean 0.3219 0.236
#> std. dev. 0.1337 0.1498
#> weight sum 73 66
#> precision 0.0052 0.0052
#>
#> V14
#> mean 0.327 0.2778
#> std. dev. 0.1673 0.1729
#> weight sum 73 66
#> precision 0.0071 0.0071
#>
#> V15
#> mean 0.3359 0.3197
#> std. dev. 0.1933 0.2224
#> weight sum 73 66
#> precision 0.0074 0.0074
#>
#> V16
#> mean 0.3919 0.3898
#> std. dev. 0.2103 0.2476
#> weight sum 73 66
#> precision 0.0073 0.0073
#>
#> V17
#> mean 0.4381 0.43
#> std. dev. 0.2464 0.2724
#> weight sum 73 66
#> precision 0.0068 0.0068
#>
#> V18
#> mean 0.4869 0.4551
#> std. dev. 0.262 0.2592
#> weight sum 73 66
#> precision 0.0068 0.0068
#>
#> V19
#> mean 0.5693 0.4702
#> std. dev. 0.2537 0.2501
#> weight sum 73 66
#> precision 0.0067 0.0067
#>
#> V2
#> mean 0.0424 0.0297
#> std. dev. 0.034 0.021
#> weight sum 73 66
#> precision 0.0012 0.0012
#>
#> V20
#> mean 0.6379 0.5048
#> std. dev. 0.238 0.2451
#> weight sum 73 66
#> precision 0.0069 0.0069
#>
#> V21
#> mean 0.6905 0.5381
#> std. dev. 0.2333 0.2365
#> weight sum 73 66
#> precision 0.0071 0.0071
#>
#> V22
#> mean 0.7055 0.5726
#> std. dev. 0.2283 0.2431
#> weight sum 73 66
#> precision 0.0065 0.0065
#>
#> V23
#> mean 0.7117 0.6198
#> std. dev. 0.2412 0.2393
#> weight sum 73 66
#> precision 0.0073 0.0073
#>
#> V24
#> mean 0.7119 0.6572
#> std. dev. 0.2413 0.2292
#> weight sum 73 66
#> precision 0.0073 0.0073
#>
#> V25
#> mean 0.7071 0.6726
#> std. dev. 0.2325 0.2255
#> weight sum 73 66
#> precision 0.007 0.007
#>
#> V26
#> mean 0.7267 0.6826
#> std. dev. 0.2286 0.223
#> weight sum 73 66
#> precision 0.0072 0.0072
#>
#> V27
#> mean 0.7251 0.6807
#> std. dev. 0.2548 0.2075
#> weight sum 73 66
#> precision 0.0076 0.0076
#>
#> V28
#> mean 0.7057 0.6807
#> std. dev. 0.262 0.1888
#> weight sum 73 66
#> precision 0.0074 0.0074
#>
#> V29
#> mean 0.637 0.6283
#> std. dev. 0.2536 0.2375
#> weight sum 73 66
#> precision 0.0074 0.0074
#>
#> V3
#> mean 0.0477 0.0358
#> std. dev. 0.033 0.0288
#> weight sum 73 66
#> precision 0.0013 0.0013
#>
#> V30
#> mean 0.5671 0.569
#> std. dev. 0.2138 0.2359
#> weight sum 73 66
#> precision 0.007 0.007
#>
#> V31
#> mean 0.4737 0.5145
#> std. dev. 0.2092 0.2132
#> weight sum 73 66
#> precision 0.0066 0.0066
#>
#> V32
#> mean 0.4119 0.4545
#> std. dev. 0.1937 0.2274
#> weight sum 73 66
#> precision 0.0065 0.0065
#>
#> V33
#> mean 0.3687 0.439
#> std. dev. 0.17 0.2305
#> weight sum 73 66
#> precision 0.0068 0.0068
#>
#> V34
#> mean 0.341 0.4295
#> std. dev. 0.1837 0.2475
#> weight sum 73 66
#> precision 0.0068 0.0068
#>
#> V35
#> mean 0.3058 0.4426
#> std. dev. 0.2175 0.2336
#> weight sum 73 66
#> precision 0.007 0.007
#>
#> V36
#> mean 0.2901 0.448
#> std. dev. 0.2152 0.2366
#> weight sum 73 66
#> precision 0.007 0.007
#>
#> V37
#> mean 0.2952 0.4111
#> std. dev. 0.1946 0.2428
#> weight sum 73 66
#> precision 0.0067 0.0067
#>
#> V38
#> mean 0.3136 0.3535
#> std. dev. 0.1716 0.2274
#> weight sum 73 66
#> precision 0.0071 0.0071
#>
#> V39
#> mean 0.333 0.3179
#> std. dev. 0.1665 0.2143
#> weight sum 73 66
#> precision 0.0069 0.0069
#>
#> V4
#> mean 0.0597 0.0438
#> std. dev. 0.0386 0.0327
#> weight sum 73 66
#> precision 0.0013 0.0013
#>
#> V40
#> mean 0.3085 0.3195
#> std. dev. 0.148 0.1967
#> weight sum 73 66
#> precision 0.0067 0.0067
#>
#> V41
#> mean 0.2795 0.2872
#> std. dev. 0.1532 0.1761
#> weight sum 73 66
#> precision 0.0063 0.0063
#>
#> V42
#> mean 0.2949 0.2405
#> std. dev. 0.1561 0.1648
#> weight sum 73 66
#> precision 0.0057 0.0057
#>
#> V43
#> mean 0.2882 0.2027
#> std. dev. 0.1388 0.1203
#> weight sum 73 66
#> precision 0.0054 0.0054
#>
#> V44
#> mean 0.2423 0.1709
#> std. dev. 0.1413 0.0942
#> weight sum 73 66
#> precision 0.0042 0.0042
#>
#> V45
#> mean 0.2182 0.1387
#> std. dev. 0.1668 0.0937
#> weight sum 73 66
#> precision 0.0052 0.0052
#>
#> V46
#> mean 0.1784 0.1172
#> std. dev. 0.1476 0.0967
#> weight sum 73 66
#> precision 0.0055 0.0055
#>
#> V47
#> mean 0.1402 0.0907
#> std. dev. 0.0969 0.0701
#> weight sum 73 66
#> precision 0.0041 0.0041
#>
#> V48
#> mean 0.1078 0.0657
#> std. dev. 0.0705 0.0493
#> weight sum 73 66
#> precision 0.0024 0.0024
#>
#> V49
#> mean 0.064 0.0374
#> std. dev. 0.0375 0.0324
#> weight sum 73 66
#> precision 0.0015 0.0015
#>
#> V5
#> mean 0.08 0.0668
#> std. dev. 0.0471 0.0521
#> weight sum 73 66
#> precision 0.0019 0.0019
#>
#> V50
#> mean 0.0225 0.0176
#> std. dev. 0.0152 0.0134
#> weight sum 73 66
#> precision 0.0007 0.0007
#>
#> V51
#> mean 0.0191 0.0138
#> std. dev. 0.0148 0.0091
#> weight sum 73 66
#> precision 0.0009 0.0009
#>
#> V52
#> mean 0.0154 0.0114
#> std. dev. 0.0112 0.0078
#> weight sum 73 66
#> precision 0.0007 0.0007
#>
#> V53
#> mean 0.0119 0.0097
#> std. dev. 0.0078 0.0065
#> weight sum 73 66
#> precision 0.0004 0.0004
#>
#> V54
#> mean 0.0121 0.0094
#> std. dev. 0.0081 0.0052
#> weight sum 73 66
#> precision 0.0003 0.0003
#>
#> V55
#> mean 0.01 0.0089
#> std. dev. 0.0083 0.0056
#> weight sum 73 66
#> precision 0.0005 0.0005
#>
#> V56
#> mean 0.0092 0.0072
#> std. dev. 0.0065 0.0047
#> weight sum 73 66
#> precision 0.0004 0.0004
#>
#> V57
#> mean 0.0074 0.0077
#> std. dev. 0.0057 0.0061
#> weight sum 73 66
#> precision 0.0004 0.0004
#>
#> V58
#> mean 0.0095 0.0068
#> std. dev. 0.0083 0.0047
#> weight sum 73 66
#> precision 0.0005 0.0005
#>
#> V59
#> mean 0.0086 0.007
#> std. dev. 0.0058 0.0045
#> weight sum 73 66
#> precision 0.0003 0.0003
#>
#> V6
#> mean 0.105 0.1017
#> std. dev. 0.0522 0.0702
#> weight sum 73 66
#> precision 0.0028 0.0028
#>
#> V60
#> mean 0.0065 0.0061
#> std. dev. 0.0042 0.0037
#> weight sum 73 66
#> precision 0.0002 0.0002
#>
#> V7
#> mean 0.1238 0.1212
#> std. dev. 0.0533 0.0684
#> weight sum 73 66
#> precision 0.0027 0.0027
#>
#> V8
#> mean 0.1414 0.1255
#> std. dev. 0.0749 0.0872
#> weight sum 73 66
#> precision 0.0031 0.0031
#>
#> V9
#> mean 0.1991 0.1475
#> std. dev. 0.1132 0.1069
#> weight sum 73 66
#> 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
#> 0.2318841