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
#>                 (0.56)  (0.44)
#> ===============================
#> V1
#>   mean           0.0385  0.0216
#>   std. dev.      0.0289  0.0136
#>   weight sum         78      61
#>   precision      0.0011  0.0011
#> 
#> V10
#>   mean           0.2647  0.1616
#>   std. dev.      0.1438  0.1237
#>   weight sum         78      61
#>   precision      0.0051  0.0051
#> 
#> V11
#>   mean           0.3096   0.177
#>   std. dev.      0.1276  0.1164
#>   weight sum         78      61
#>   precision      0.0051  0.0051
#> 
#> V12
#>   mean           0.3217  0.1872
#>   std. dev.      0.1205  0.1338
#>   weight sum         78      61
#>   precision      0.0049  0.0049
#> 
#> V13
#>   mean           0.3317  0.2256
#>   std. dev.      0.1307  0.1325
#>   weight sum         78      61
#>   precision      0.0052  0.0052
#> 
#> V14
#>   mean           0.3254  0.2671
#>   std. dev.      0.1662  0.1502
#>   weight sum         78      61
#>   precision      0.0071  0.0071
#> 
#> V15
#>   mean           0.3272  0.2952
#>   std. dev.      0.1935  0.2066
#>   weight sum         78      61
#>   precision      0.0067  0.0067
#> 
#> V16
#>   mean           0.3745  0.3663
#>   std. dev.       0.212  0.2467
#>   weight sum         78      61
#>   precision      0.0071  0.0071
#> 
#> V17
#>   mean           0.4078  0.4093
#>   std. dev.      0.2358  0.2898
#>   weight sum         78      61
#>   precision       0.007   0.007
#> 
#> V18
#>   mean            0.445  0.4516
#>   std. dev.      0.2507  0.2679
#>   weight sum         78      61
#>   precision      0.0068  0.0068
#> 
#> V19
#>   mean           0.5239  0.4779
#>   std. dev.      0.2476  0.2546
#>   weight sum         78      61
#>   precision       0.007   0.007
#> 
#> V2
#>   mean           0.0487  0.0306
#>   std. dev.      0.0416  0.0269
#>   weight sum         78      61
#>   precision      0.0018  0.0018
#> 
#> V20
#>   mean           0.6013  0.5081
#>   std. dev.      0.2556   0.257
#>   weight sum         78      61
#>   precision      0.0069  0.0069
#> 
#> V21
#>   mean           0.6485  0.5555
#>   std. dev.      0.2563  0.2439
#>   weight sum         78      61
#>   precision      0.0071  0.0071
#> 
#> V22
#>   mean           0.6589  0.5941
#>   std. dev.      0.2372  0.2455
#>   weight sum         78      61
#>   precision      0.0072  0.0072
#> 
#> V23
#>   mean           0.6777  0.6233
#>   std. dev.      0.2543  0.2417
#>   weight sum         78      61
#>   precision      0.0071  0.0071
#> 
#> V24
#>   mean           0.7039  0.6471
#>   std. dev.      0.2444   0.215
#>   weight sum         78      61
#>   precision      0.0072  0.0072
#> 
#> V25
#>   mean           0.7049  0.6579
#>   std. dev.      0.2352  0.2227
#>   weight sum         78      61
#>   precision      0.0074  0.0074
#> 
#> V26
#>   mean            0.723  0.6817
#>   std. dev.      0.2385  0.2242
#>   weight sum         78      61
#>   precision       0.007   0.007
#> 
#> V27
#>   mean           0.7207  0.6853
#>   std. dev.      0.2742  0.2128
#>   weight sum         78      61
#>   precision      0.0074  0.0074
#> 
#> V28
#>   mean           0.7125  0.6846
#>   std. dev.      0.2695  0.2223
#>   weight sum         78      61
#>   precision      0.0076  0.0076
#> 
#> V29
#>   mean           0.6476  0.6414
#>   std. dev.      0.2527  0.2441
#>   weight sum         78      61
#>   precision      0.0075  0.0075
#> 
#> V3
#>   mean           0.0536  0.0375
#>   std. dev.       0.048  0.0314
#>   weight sum         78      61
#>   precision      0.0023  0.0023
#> 
#> V30
#>   mean           0.5786  0.5914
#>   std. dev.      0.2116  0.2314
#>   weight sum         78      61
#>   precision      0.0069  0.0069
#> 
#> V31
#>   mean           0.4969  0.5379
#>   std. dev.      0.2257  0.2008
#>   weight sum         78      61
#>   precision      0.0067  0.0067
#> 
#> V32
#>   mean           0.4466  0.4531
#>   std. dev.      0.2191  0.2154
#>   weight sum         78      61
#>   precision      0.0064  0.0064
#> 
#> V33
#>   mean           0.4086  0.4308
#>   std. dev.       0.201  0.2144
#>   weight sum         78      61
#>   precision       0.007   0.007
#> 
#> V34
#>   mean           0.3688  0.4307
#>   std. dev.      0.2136  0.2494
#>   weight sum         78      61
#>   precision      0.0069  0.0069
#> 
#> V35
#>   mean           0.3293   0.454
#>   std. dev.      0.2482  0.2615
#>   weight sum         78      61
#>   precision      0.0071  0.0071
#> 
#> V36
#>   mean           0.3124  0.4727
#>   std. dev.      0.2372  0.2498
#>   weight sum         78      61
#>   precision      0.0073  0.0073
#> 
#> V37
#>   mean           0.3097  0.4232
#>   std. dev.      0.2174   0.232
#>   weight sum         78      61
#>   precision      0.0066  0.0066
#> 
#> V38
#>   mean           0.3273  0.3635
#>   std. dev.      0.1869   0.228
#>   weight sum         78      61
#>   precision      0.0071  0.0071
#> 
#> V39
#>   mean           0.3435  0.3267
#>   std. dev.       0.182   0.219
#>   weight sum         78      61
#>   precision      0.0069  0.0069
#> 
#> V4
#>   mean           0.0658  0.0441
#>   std. dev.      0.0606  0.0326
#>   weight sum         78      61
#>   precision      0.0034  0.0034
#> 
#> V40
#>   mean            0.313  0.3183
#>   std. dev.      0.1672   0.196
#>   weight sum         78      61
#>   precision      0.0067  0.0067
#> 
#> V41
#>   mean           0.2964  0.2778
#>   std. dev.      0.1654  0.1715
#>   weight sum         78      61
#>   precision      0.0054  0.0054
#> 
#> V42
#>   mean           0.3091  0.2463
#>   std. dev.      0.1809  0.1576
#>   weight sum         78      61
#>   precision      0.0059  0.0059
#> 
#> V43
#>   mean           0.2877  0.2185
#>   std. dev.      0.1518  0.1341
#>   weight sum         78      61
#>   precision      0.0056  0.0056
#> 
#> V44
#>   mean           0.2527   0.178
#>   std. dev.      0.1487   0.122
#>   weight sum         78      61
#>   precision      0.0058  0.0058
#> 
#> V45
#>   mean           0.2526  0.1419
#>   std. dev.      0.1805  0.1019
#>   weight sum         78      61
#>   precision      0.0051  0.0051
#> 
#> V46
#>   mean           0.1984  0.1145
#>   std. dev.      0.1601  0.0856
#>   weight sum         78      61
#>   precision      0.0054  0.0054
#> 
#> V47
#>   mean           0.1474  0.0955
#>   std. dev.       0.098  0.0603
#>   weight sum         78      61
#>   precision      0.0041  0.0041
#> 
#> V48
#>   mean           0.1146  0.0718
#>   std. dev.        0.07  0.0435
#>   weight sum         78      61
#>   precision      0.0025  0.0025
#> 
#> V49
#>   mean           0.0677  0.0375
#>   std. dev.      0.0388  0.0269
#>   weight sum         78      61
#>   precision      0.0014  0.0014
#> 
#> V5
#>   mean           0.0882  0.0619
#>   std. dev.      0.0633  0.0498
#>   weight sum         78      61
#>   precision       0.003   0.003
#> 
#> V50
#>   mean           0.0239  0.0182
#>   std. dev.      0.0154  0.0115
#>   weight sum         78      61
#>   precision      0.0007  0.0007
#> 
#> V51
#>   mean           0.0195  0.0125
#>   std. dev.      0.0149  0.0083
#>   weight sum         78      61
#>   precision      0.0008  0.0008
#> 
#> V52
#>   mean           0.0156  0.0114
#>   std. dev.      0.0107  0.0072
#>   weight sum         78      61
#>   precision      0.0007  0.0007
#> 
#> V53
#>   mean           0.0119  0.0101
#>   std. dev.       0.008  0.0067
#>   weight sum         78      61
#>   precision      0.0004  0.0004
#> 
#> V54
#>   mean           0.0116    0.01
#>   std. dev.      0.0082  0.0057
#>   weight sum         78      61
#>   precision      0.0004  0.0004
#> 
#> V55
#>   mean            0.011  0.0084
#>   std. dev.      0.0086  0.0052
#>   weight sum         78      61
#>   precision      0.0004  0.0004
#> 
#> V56
#>   mean           0.0089  0.0073
#>   std. dev.      0.0064   0.005
#>   weight sum         78      61
#>   precision      0.0004  0.0004
#> 
#> V57
#>   mean           0.0079   0.008
#>   std. dev.      0.0059  0.0059
#>   weight sum         78      61
#>   precision      0.0004  0.0004
#> 
#> V58
#>   mean           0.0093  0.0071
#>   std. dev.      0.0076  0.0049
#>   weight sum         78      61
#>   precision      0.0005  0.0005
#> 
#> V59
#>   mean           0.0085  0.0074
#>   std. dev.      0.0062  0.0048
#>   weight sum         78      61
#>   precision      0.0003  0.0003
#> 
#> V6
#>   mean           0.1115  0.0932
#>   std. dev.      0.0512  0.0651
#>   weight sum         78      61
#>   precision      0.0028  0.0028
#> 
#> V60
#>   mean           0.0066  0.0065
#>   std. dev.      0.0047  0.0039
#>   weight sum         78      61
#>   precision      0.0003  0.0003
#> 
#> V7
#>   mean           0.1306  0.1114
#>   std. dev.      0.0569   0.065
#>   weight sum         78      61
#>   precision      0.0028  0.0028
#> 
#> V8
#>   mean           0.1568  0.1172
#>   std. dev.       0.081  0.0809
#>   weight sum         78      61
#>   precision      0.0034  0.0034
#> 
#> V9
#>   mean           0.2159  0.1457
#>   std. dev.       0.114  0.1067
#>   weight sum         78      61
#>   precision      0.0041  0.0041
#> 
#> 


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

# Score the predictions
predictions$score()
#> classif.ce 
#>  0.3043478