Classification Naive Bayes Learner From Weka

Naive 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

Dictionary

This Learner can be instantiated via lrn():

lrn("classif.naive_bayes_weka")

Meta Information

• Task type: “classif”

• Predict Types: “response”, “prob”

• Feature Types: “logical”, “integer”, “numeric”, “factor”, “ordered”

• Required Packages: mlr3, RWeka

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

• Dictionary of Learners: mlr3::mlr_learners.

• 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.

Author

damirpolat

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifNaiveBayesWeka

Active bindings

marshaled

(logical(1))
Whether the learner has been marshaled.

Methods

Public methods

• LearnerClassifNaiveBayesWeka$new()

• LearnerClassifNaiveBayesWeka$marshal()

• LearnerClassifNaiveBayesWeka$unmarshal()

• LearnerClassifNaiveBayesWeka$clone()

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()

---

Method new()

Creates a new instance of this R6 class.

Usage

LearnerClassifNaiveBayesWeka$new()

---

Method marshal()

Marshal the learner's model.

Usage

LearnerClassifNaiveBayesWeka$marshal(...)

Arguments

...

(any)
Additional arguments passed to mlr3::marshal_model().

---

Method unmarshal()

Unmarshal the learner's model.

Usage

LearnerClassifNaiveBayesWeka$unmarshal(...)

Arguments

...

(any)
Additional arguments passed to mlr3::unmarshal_model().

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClassifNaiveBayesWeka$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

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'

# 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
#>                 (0.57)  (0.43)
#> ===============================
#> V1
#>   mean           0.0377  0.0236
#>   std. dev.      0.0289  0.0151
#>   weight sum         79      60
#>   precision      0.0011  0.0011
#> 
#> V10
#>   mean           0.2592  0.1749
#>   std. dev.      0.1492  0.1196
#>   weight sum         79      60
#>   precision      0.0051  0.0051
#> 
#> V11
#>   mean           0.2945  0.1899
#>   std. dev.      0.1383  0.1245
#>   weight sum         79      60
#>   precision      0.0052  0.0052
#> 
#> V12
#>   mean           0.2961  0.1999
#>   std. dev.      0.1319  0.1501
#>   weight sum         79      60
#>   precision      0.0049  0.0049
#> 
#> V13
#>   mean           0.3107  0.2332
#>   std. dev.      0.1354  0.1452
#>   weight sum         79      60
#>   precision      0.0052  0.0052
#> 
#> V14
#>   mean           0.3183  0.2783
#>   std. dev.      0.1633  0.1705
#>   weight sum         79      60
#>   precision      0.0072  0.0072
#> 
#> V15
#>   mean           0.3399  0.3212
#>   std. dev.      0.1843  0.2265
#>   weight sum         79      60
#>   precision      0.0067  0.0067
#> 
#> V16
#>   mean           0.3844  0.3876
#>   std. dev.      0.2015  0.2726
#>   weight sum         79      60
#>   precision      0.0071  0.0071
#> 
#> V17
#>   mean           0.4164  0.4258
#>   std. dev.      0.2339  0.2994
#>   weight sum         79      60
#>   precision      0.0072  0.0072
#> 
#> V18
#>   mean           0.4521   0.461
#>   std. dev.       0.258  0.2756
#>   weight sum         79      60
#>   precision      0.0068  0.0068
#> 
#> V19
#>   mean             0.54  0.4841
#>   std. dev.      0.2548  0.2627
#>   weight sum         79      60
#>   precision      0.0066  0.0066
#> 
#> V2
#>   mean           0.0506  0.0303
#>   std. dev.        0.04  0.0217
#>   weight sum         79      60
#>   precision      0.0018  0.0018
#> 
#> V20
#>   mean           0.6241  0.5217
#>   std. dev.      0.2515  0.2549
#>   weight sum         79      60
#>   precision      0.0068  0.0068
#> 
#> V21
#>   mean           0.6757  0.5619
#>   std. dev.      0.2387  0.2486
#>   weight sum         79      60
#>   precision      0.0071  0.0071
#> 
#> V22
#>   mean           0.6775  0.5894
#>   std. dev.      0.2294  0.2624
#>   weight sum         79      60
#>   precision      0.0069  0.0069
#> 
#> V23
#>   mean           0.6735   0.624
#>   std. dev.        0.25  0.2468
#>   weight sum         79      60
#>   precision       0.007   0.007
#> 
#> V24
#>   mean           0.6819  0.6608
#>   std. dev.      0.2476  0.2355
#>   weight sum         79      60
#>   precision      0.0073  0.0073
#> 
#> V25
#>   mean           0.6733   0.667
#>   std. dev.      0.2446  0.2483
#>   weight sum         79      60
#>   precision      0.0073  0.0073
#> 
#> V26
#>   mean           0.6944  0.6928
#>   std. dev.       0.238  0.2334
#>   weight sum         79      60
#>   precision      0.0065  0.0065
#> 
#> V27
#>   mean           0.6997  0.7008
#>   std. dev.      0.2695  0.1928
#>   weight sum         79      60
#>   precision      0.0071  0.0071
#> 
#> V28
#>   mean           0.7018  0.6715
#>   std. dev.      0.2626  0.1874
#>   weight sum         79      60
#>   precision      0.0075  0.0075
#> 
#> V29
#>   mean           0.6392  0.6205
#>   std. dev.      0.2458  0.2501
#>   weight sum         79      60
#>   precision      0.0074  0.0074
#> 
#> V3
#>   mean           0.0566  0.0395
#>   std. dev.      0.0476  0.0292
#>   weight sum         79      60
#>   precision      0.0024  0.0024
#> 
#> V30
#>   mean           0.5779  0.5761
#>   std. dev.      0.2127  0.2519
#>   weight sum         79      60
#>   precision       0.007   0.007
#> 
#> V31
#>   mean            0.481  0.5432
#>   std. dev.      0.2122  0.1975
#>   weight sum         79      60
#>   precision      0.0062  0.0062
#> 
#> V32
#>   mean            0.424   0.482
#>   std. dev.      0.1988   0.211
#>   weight sum         79      60
#>   precision      0.0062  0.0062
#> 
#> V33
#>   mean           0.3915  0.4725
#>   std. dev.      0.1742  0.2181
#>   weight sum         79      60
#>   precision      0.0068  0.0068
#> 
#> V34
#>   mean           0.3724  0.4725
#>   std. dev.      0.1908    0.24
#>   weight sum         79      60
#>   precision      0.0067  0.0067
#> 
#> V35
#>   mean           0.3489  0.4704
#>   std. dev.       0.247  0.2499
#>   weight sum         79      60
#>   precision      0.0071  0.0071
#> 
#> V36
#>   mean           0.3245  0.4726
#>   std. dev.      0.2563  0.2527
#>   weight sum         79      60
#>   precision      0.0072  0.0072
#> 
#> V37
#>   mean           0.3198  0.4403
#>   std. dev.      0.2294  0.2402
#>   weight sum         79      60
#>   precision      0.0067  0.0067
#> 
#> V38
#>   mean           0.3353   0.351
#>   std. dev.      0.2074   0.218
#>   weight sum         79      60
#>   precision       0.007   0.007
#> 
#> V39
#>   mean           0.3377  0.3115
#>   std. dev.      0.1814  0.1896
#>   weight sum         79      60
#>   precision      0.0062  0.0062
#> 
#> V4
#>   mean           0.0715  0.0452
#>   std. dev.        0.06  0.0333
#>   weight sum         79      60
#>   precision      0.0034  0.0034
#> 
#> V40
#>   mean           0.2981  0.3391
#>   std. dev.      0.1506  0.1613
#>   weight sum         79      60
#>   precision       0.005   0.005
#> 
#> V41
#>   mean           0.2892  0.2988
#>   std. dev.      0.1575  0.1634
#>   weight sum         79      60
#>   precision      0.0051  0.0051
#> 
#> V42
#>   mean           0.3022   0.252
#>   std. dev.      0.1647  0.1602
#>   weight sum         79      60
#>   precision      0.0057  0.0057
#> 
#> V43
#>   mean             0.28  0.2066
#>   std. dev.       0.144  0.1198
#>   weight sum         79      60
#>   precision      0.0055  0.0055
#> 
#> V44
#>   mean           0.2568  0.1753
#>   std. dev.      0.1389   0.091
#>   weight sum         79      60
#>   precision      0.0041  0.0041
#> 
#> V45
#>   mean           0.2529    0.14
#>   std. dev.      0.1727  0.0785
#>   weight sum         79      60
#>   precision      0.0051  0.0051
#> 
#> V46
#>   mean           0.1996  0.1096
#>   std. dev.      0.1554  0.0759
#>   weight sum         79      60
#>   precision      0.0054  0.0054
#> 
#> V47
#>   mean           0.1439  0.0897
#>   std. dev.       0.095  0.0551
#>   weight sum         79      60
#>   precision      0.0041  0.0041
#> 
#> V48
#>   mean           0.1078  0.0675
#>   std. dev.      0.0666  0.0427
#>   weight sum         79      60
#>   precision      0.0025  0.0025
#> 
#> V49
#>   mean           0.0626  0.0377
#>   std. dev.      0.0365  0.0265
#>   weight sum         79      60
#>   precision      0.0014  0.0014
#> 
#> V5
#>   mean           0.0857  0.0689
#>   std. dev.      0.0647  0.0537
#>   weight sum         79      60
#>   precision       0.003   0.003
#> 
#> V50
#>   mean           0.0225  0.0157
#>   std. dev.      0.0149  0.0098
#>   weight sum         79      60
#>   precision      0.0008  0.0008
#> 
#> V51
#>   mean           0.0196  0.0114
#>   std. dev.      0.0149  0.0083
#>   weight sum         79      60
#>   precision      0.0008  0.0008
#> 
#> V52
#>   mean           0.0164  0.0106
#>   std. dev.      0.0111  0.0071
#>   weight sum         79      60
#>   precision      0.0007  0.0007
#> 
#> V53
#>   mean           0.0122  0.0099
#>   std. dev.      0.0081   0.006
#>   weight sum         79      60
#>   precision      0.0004  0.0004
#> 
#> V54
#>   mean           0.0125   0.009
#>   std. dev.      0.0086  0.0047
#>   weight sum         79      60
#>   precision      0.0004  0.0004
#> 
#> V55
#>   mean           0.0102  0.0083
#>   std. dev.      0.0089  0.0053
#>   weight sum         79      60
#>   precision      0.0004  0.0004
#> 
#> V56
#>   mean           0.0093  0.0074
#>   std. dev.      0.0069  0.0051
#>   weight sum         79      60
#>   precision      0.0004  0.0004
#> 
#> V57
#>   mean           0.0084   0.008
#>   std. dev.      0.0062  0.0065
#>   weight sum         79      60
#>   precision      0.0004  0.0004
#> 
#> V58
#>   mean           0.0093  0.0062
#>   std. dev.      0.0079  0.0045
#>   weight sum         79      60
#>   precision      0.0005  0.0005
#> 
#> V59
#>   mean           0.0091  0.0072
#>   std. dev.      0.0073  0.0047
#>   weight sum         79      60
#>   precision      0.0004  0.0004
#> 
#> V6
#>   mean           0.1113  0.1059
#>   std. dev.      0.0459  0.0757
#>   weight sum         79      60
#>   precision      0.0028  0.0028
#> 
#> V60
#>   mean           0.0077  0.0062
#>   std. dev.      0.0066  0.0036
#>   weight sum         79      60
#>   precision      0.0005  0.0005
#> 
#> V7
#>   mean           0.1326  0.1168
#>   std. dev.      0.0594  0.0725
#>   weight sum         79      60
#>   precision      0.0028  0.0028
#> 
#> V8
#>   mean           0.1518  0.1225
#>   std. dev.      0.0932   0.088
#>   weight sum         79      60
#>   precision      0.0034  0.0034
#> 
#> V9
#>   mean           0.2171  0.1508
#>   std. dev.      0.1299   0.108
#>   weight sum         79      60
#>   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 
#>  0.3043478