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
#>                 (0.55)  (0.45)
#> ===============================
#> V1
#>   mean           0.0368  0.0218
#>   std. dev.      0.0275  0.0124
#>   weight sum         77      62
#>   precision      0.0011  0.0011
#> 
#> V10
#>   mean           0.2455  0.1644
#>   std. dev.      0.1312  0.1265
#>   weight sum         77      62
#>   precision      0.0051  0.0051
#> 
#> V11
#>   mean           0.2901  0.1766
#>   std. dev.      0.1215  0.1192
#>   weight sum         77      62
#>   precision      0.0052  0.0052
#> 
#> V12
#>   mean           0.3087  0.2035
#>   std. dev.      0.1217  0.1442
#>   weight sum         77      62
#>   precision       0.005   0.005
#> 
#> V13
#>   mean           0.3263  0.2384
#>   std. dev.       0.127  0.1491
#>   weight sum         77      62
#>   precision      0.0053  0.0053
#> 
#> V14
#>   mean           0.3462  0.2943
#>   std. dev.      0.1586  0.1762
#>   weight sum         77      62
#>   precision      0.0069  0.0069
#> 
#> V15
#>   mean           0.3462   0.334
#>   std. dev.      0.1868  0.2308
#>   weight sum         77      62
#>   precision      0.0072  0.0072
#> 
#> V16
#>   mean           0.3935  0.3913
#>   std. dev.      0.2066  0.2643
#>   weight sum         77      62
#>   precision       0.007   0.007
#> 
#> V17
#>   mean           0.4475  0.4249
#>   std. dev.      0.2379  0.2902
#>   weight sum         77      62
#>   precision      0.0071  0.0071
#> 
#> V18
#>   mean           0.4979  0.4545
#>   std. dev.      0.2502  0.2642
#>   weight sum         77      62
#>   precision      0.0071  0.0071
#> 
#> V19
#>   mean           0.5721   0.464
#>   std. dev.      0.2557   0.256
#>   weight sum         77      62
#>   precision      0.0068  0.0068
#> 
#> V2
#>   mean           0.0441  0.0326
#>   std. dev.      0.0377  0.0262
#>   weight sum         77      62
#>   precision      0.0018  0.0018
#> 
#> V20
#>   mean           0.6384  0.5006
#>   std. dev.      0.2541  0.2621
#>   weight sum         77      62
#>   precision      0.0068  0.0068
#> 
#> V21
#>   mean            0.681  0.5375
#>   std. dev.       0.256  0.2461
#>   weight sum         77      62
#>   precision      0.0071  0.0071
#> 
#> V22
#>   mean            0.692  0.5593
#>   std. dev.      0.2459  0.2561
#>   weight sum         77      62
#>   precision      0.0072  0.0072
#> 
#> V23
#>   mean           0.6941  0.5948
#>   std. dev.      0.2554  0.2461
#>   weight sum         77      62
#>   precision      0.0071  0.0071
#> 
#> V24
#>   mean           0.7053   0.637
#>   std. dev.      0.2481  0.2437
#>   weight sum         77      62
#>   precision      0.0073  0.0073
#> 
#> V25
#>   mean           0.6989  0.6471
#>   std. dev.      0.2324  0.2664
#>   weight sum         77      62
#>   precision      0.0075  0.0075
#> 
#> V26
#>   mean           0.7135  0.6695
#>   std. dev.      0.2279  0.2461
#>   weight sum         77      62
#>   precision      0.0069  0.0069
#> 
#> V27
#>   mean           0.7081   0.677
#>   std. dev.      0.2615  0.2266
#>   weight sum         77      62
#>   precision      0.0076  0.0076
#> 
#> V28
#>   mean           0.6838  0.6676
#>   std. dev.      0.2642  0.2001
#>   weight sum         77      62
#>   precision      0.0074  0.0074
#> 
#> V29
#>   mean           0.6235  0.6196
#>   std. dev.      0.2594  0.2332
#>   weight sum         77      62
#>   precision      0.0074  0.0074
#> 
#> V3
#>   mean            0.052   0.039
#>   std. dev.      0.0486  0.0335
#>   weight sum         77      62
#>   precision      0.0023  0.0023
#> 
#> V30
#>   mean           0.5556  0.5648
#>   std. dev.      0.2176  0.2261
#>   weight sum         77      62
#>   precision       0.007   0.007
#> 
#> V31
#>   mean           0.4634  0.5185
#>   std. dev.      0.2297  0.1928
#>   weight sum         77      62
#>   precision      0.0066  0.0066
#> 
#> V32
#>   mean           0.4153  0.4292
#>   std. dev.      0.2256  0.2129
#>   weight sum         77      62
#>   precision      0.0063  0.0063
#> 
#> V33
#>   mean           0.3998  0.4056
#>   std. dev.      0.2005  0.2016
#>   weight sum         77      62
#>   precision       0.007   0.007
#> 
#> V34
#>   mean           0.3705  0.4095
#>   std. dev.      0.2128  0.2479
#>   weight sum         77      62
#>   precision      0.0069  0.0069
#> 
#> V35
#>   mean           0.3422  0.4314
#>   std. dev.      0.2358  0.2591
#>   weight sum         77      62
#>   precision      0.0071  0.0071
#> 
#> V36
#>   mean           0.3222  0.4551
#>   std. dev.      0.2314  0.2653
#>   weight sum         77      62
#>   precision      0.0073  0.0073
#> 
#> V37
#>   mean           0.3146  0.4235
#>   std. dev.      0.2113  0.2546
#>   weight sum         77      62
#>   precision      0.0066  0.0066
#> 
#> V38
#>   mean           0.3245  0.3743
#>   std. dev.      0.1806  0.2339
#>   weight sum         77      62
#>   precision       0.007   0.007
#> 
#> V39
#>   mean           0.3301  0.3166
#>   std. dev.      0.1704  0.2314
#>   weight sum         77      62
#>   precision       0.007   0.007
#> 
#> V4
#>   mean           0.0705  0.0427
#>   std. dev.        0.06  0.0356
#>   weight sum         77      62
#>   precision      0.0033  0.0033
#> 
#> V40
#>   mean           0.3055  0.3145
#>   std. dev.      0.1597  0.2012
#>   weight sum         77      62
#>   precision      0.0067  0.0067
#> 
#> V41
#>   mean           0.3019  0.2899
#>   std. dev.      0.1598  0.1824
#>   weight sum         77      62
#>   precision      0.0063  0.0063
#> 
#> V42
#>   mean           0.3166  0.2586
#>   std. dev.       0.165  0.1626
#>   weight sum         77      62
#>   precision      0.0059  0.0059
#> 
#> V43
#>   mean           0.2791  0.2142
#>   std. dev.      0.1354  0.1085
#>   weight sum         77      62
#>   precision      0.0053  0.0053
#> 
#> V44
#>   mean           0.2395  0.1698
#>   std. dev.      0.1444   0.079
#>   weight sum         77      62
#>   precision      0.0042  0.0042
#> 
#> V45
#>   mean           0.2351  0.1409
#>   std. dev.      0.1691  0.0841
#>   weight sum         77      62
#>   precision      0.0047  0.0047
#> 
#> V46
#>   mean           0.1876  0.1175
#>   std. dev.      0.1491  0.0938
#>   weight sum         77      62
#>   precision      0.0054  0.0054
#> 
#> V47
#>   mean           0.1397  0.0985
#>   std. dev.      0.0911  0.0677
#>   weight sum         77      62
#>   precision      0.0041  0.0041
#> 
#> V48
#>   mean           0.1063  0.0743
#>   std. dev.      0.0648  0.0491
#>   weight sum         77      62
#>   precision      0.0024  0.0024
#> 
#> V49
#>   mean           0.0608  0.0423
#>   std. dev.      0.0344   0.032
#>   weight sum         77      62
#>   precision      0.0015  0.0015
#> 
#> V5
#>   mean           0.0976  0.0612
#>   std. dev.      0.0651   0.051
#>   weight sum         77      62
#>   precision       0.003   0.003
#> 
#> V50
#>   mean           0.0223  0.0194
#>   std. dev.      0.0137  0.0142
#>   weight sum         77      62
#>   precision      0.0007  0.0007
#> 
#> V51
#>   mean           0.0183  0.0128
#>   std. dev.      0.0131  0.0092
#>   weight sum         77      62
#>   precision      0.0009  0.0009
#> 
#> V52
#>   mean           0.0154   0.011
#>   std. dev.      0.0109  0.0067
#>   weight sum         77      62
#>   precision      0.0006  0.0006
#> 
#> V53
#>   mean           0.0123  0.0097
#>   std. dev.      0.0081  0.0062
#>   weight sum         77      62
#>   precision      0.0004  0.0004
#> 
#> V54
#>   mean           0.0127  0.0097
#>   std. dev.      0.0086  0.0052
#>   weight sum         77      62
#>   precision      0.0003  0.0003
#> 
#> V55
#>   mean           0.0101  0.0092
#>   std. dev.      0.0087  0.0055
#>   weight sum         77      62
#>   precision      0.0004  0.0004
#> 
#> V56
#>   mean           0.0094   0.007
#>   std. dev.      0.0064  0.0046
#>   weight sum         77      62
#>   precision      0.0004  0.0004
#> 
#> V57
#>   mean           0.0081  0.0075
#>   std. dev.      0.0061  0.0053
#>   weight sum         77      62
#>   precision      0.0004  0.0004
#> 
#> V58
#>   mean           0.0094  0.0062
#>   std. dev.       0.008  0.0044
#>   weight sum         77      62
#>   precision      0.0004  0.0004
#> 
#> V59
#>   mean           0.0089  0.0068
#>   std. dev.      0.0072  0.0048
#>   weight sum         77      62
#>   precision      0.0004  0.0004
#> 
#> V6
#>   mean           0.1159  0.0995
#>   std. dev.      0.0532  0.0725
#>   weight sum         77      62
#>   precision      0.0028  0.0028
#> 
#> V60
#>   mean           0.0069   0.006
#>   std. dev.      0.0063  0.0037
#>   weight sum         77      62
#>   precision      0.0006  0.0006
#> 
#> V7
#>   mean           0.1277  0.1236
#>   std. dev.      0.0546  0.0712
#>   weight sum         77      62
#>   precision      0.0028  0.0028
#> 
#> V8
#>   mean           0.1439  0.1273
#>   std. dev.      0.0717  0.0853
#>   weight sum         77      62
#>   precision      0.0031  0.0031
#> 
#> V9
#>   mean           0.2067  0.1476
#>   std. dev.      0.1095  0.1129
#>   weight sum         77      62
#>   precision      0.0042  0.0042
#> 
#> 


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()
#> classif.ce 
#>  0.2898551