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/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.

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', 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
#>                 (0.55)  (0.45)
#> ===============================
#> V1
#>   mean            0.037  0.0221
#>   std. dev.      0.0279  0.0144
#>   weight sum         76      63
#>   precision      0.0011  0.0011
#> 
#> V10
#>   mean           0.2482  0.1561
#>   std. dev.       0.137  0.0999
#>   weight sum         76      63
#>   precision      0.0051  0.0051
#> 
#> V11
#>   mean           0.2897  0.1706
#>   std. dev.      0.1253   0.108
#>   weight sum         76      63
#>   precision      0.0052  0.0052
#> 
#> V12
#>   mean           0.3086  0.1904
#>   std. dev.      0.1292  0.1326
#>   weight sum         76      63
#>   precision      0.0046  0.0046
#> 
#> V13
#>   mean           0.3229   0.228
#>   std. dev.      0.1333   0.138
#>   weight sum         76      63
#>   precision      0.0052  0.0052
#> 
#> V14
#>   mean           0.3325  0.2655
#>   std. dev.      0.1713  0.1644
#>   weight sum         76      63
#>   precision      0.0072  0.0072
#> 
#> V15
#>   mean           0.3407  0.3074
#>   std. dev.      0.1992  0.2195
#>   weight sum         76      63
#>   precision      0.0067  0.0067
#> 
#> V16
#>   mean           0.3886  0.3775
#>   std. dev.       0.219   0.266
#>   weight sum         76      63
#>   precision      0.0071  0.0071
#> 
#> V17
#>   mean            0.428   0.411
#>   std. dev.      0.2442  0.3031
#>   weight sum         76      63
#>   precision       0.007   0.007
#> 
#> V18
#>   mean           0.4726  0.4415
#>   std. dev.       0.259  0.2774
#>   weight sum         76      63
#>   precision      0.0068  0.0068
#> 
#> V19
#>   mean           0.5558  0.4631
#>   std. dev.      0.2668  0.2566
#>   weight sum         76      63
#>   precision       0.007   0.007
#> 
#> V2
#>   mean           0.0479  0.0298
#>   std. dev.      0.0388  0.0249
#>   weight sum         76      63
#>   precision      0.0018  0.0018
#> 
#> V20
#>   mean           0.6302  0.4905
#>   std. dev.      0.2599  0.2489
#>   weight sum         76      63
#>   precision      0.0069  0.0069
#> 
#> V21
#>   mean           0.6717  0.5322
#>   std. dev.      0.2616  0.2355
#>   weight sum         76      63
#>   precision      0.0071  0.0071
#> 
#> V22
#>   mean           0.6792  0.5636
#>   std. dev.      0.2502  0.2648
#>   weight sum         76      63
#>   precision      0.0072  0.0072
#> 
#> V23
#>   mean           0.6854  0.6056
#>   std. dev.      0.2463  0.2554
#>   weight sum         76      63
#>   precision       0.007   0.007
#> 
#> V24
#>   mean           0.6936  0.6497
#>   std. dev.      0.2311  0.2348
#>   weight sum         76      63
#>   precision      0.0073  0.0073
#> 
#> V25
#>   mean           0.6779  0.6669
#>   std. dev.      0.2248  0.2657
#>   weight sum         76      63
#>   precision      0.0075  0.0075
#> 
#> V26
#>   mean           0.6896  0.6863
#>   std. dev.      0.2327  0.2551
#>   weight sum         76      63
#>   precision       0.007   0.007
#> 
#> V27
#>   mean            0.689  0.6724
#>   std. dev.      0.2727  0.2406
#>   weight sum         76      63
#>   precision      0.0076  0.0076
#> 
#> V28
#>   mean            0.676  0.6741
#>   std. dev.      0.2728  0.1975
#>   weight sum         76      63
#>   precision      0.0075  0.0075
#> 
#> V29
#>   mean            0.624  0.6564
#>   std. dev.      0.2506  0.2215
#>   weight sum         76      63
#>   precision      0.0075  0.0075
#> 
#> V3
#>   mean           0.0528  0.0353
#>   std. dev.      0.0479  0.0285
#>   weight sum         76      63
#>   precision      0.0023  0.0023
#> 
#> V30
#>   mean            0.564  0.6013
#>   std. dev.      0.2103  0.2087
#>   weight sum         76      63
#>   precision       0.007   0.007
#> 
#> V31
#>   mean           0.4811  0.5366
#>   std. dev.      0.2223  0.1912
#>   weight sum         76      63
#>   precision      0.0063  0.0063
#> 
#> V32
#>   mean           0.4168  0.4367
#>   std. dev.      0.2263  0.2142
#>   weight sum         76      63
#>   precision      0.0065  0.0065
#> 
#> V33
#>   mean            0.384  0.4321
#>   std. dev.      0.2005  0.2116
#>   weight sum         76      63
#>   precision       0.007   0.007
#> 
#> V34
#>   mean            0.362   0.458
#>   std. dev.      0.1986  0.2498
#>   weight sum         76      63
#>   precision      0.0068  0.0068
#> 
#> V35
#>   mean           0.3305  0.4618
#>   std. dev.      0.2299   0.268
#>   weight sum         76      63
#>   precision      0.0071  0.0071
#> 
#> V36
#>   mean            0.313  0.4603
#>   std. dev.      0.2232  0.2649
#>   weight sum         76      63
#>   precision      0.0072  0.0072
#> 
#> V37
#>   mean           0.3071  0.4062
#>   std. dev.      0.2119  0.2511
#>   weight sum         76      63
#>   precision      0.0066  0.0066
#> 
#> V38
#>   mean           0.3291  0.3595
#>   std. dev.      0.1864  0.2384
#>   weight sum         76      63
#>   precision       0.007   0.007
#> 
#> V39
#>   mean           0.3476  0.3133
#>   std. dev.      0.1823   0.238
#>   weight sum         76      63
#>   precision      0.0069  0.0069
#> 
#> V4
#>   mean           0.0671  0.0419
#>   std. dev.      0.0595  0.0322
#>   weight sum         76      63
#>   precision      0.0034  0.0034
#> 
#> V40
#>   mean           0.3144  0.3209
#>   std. dev.      0.1694  0.2076
#>   weight sum         76      63
#>   precision      0.0067  0.0067
#> 
#> V41
#>   mean           0.2954  0.2883
#>   std. dev.       0.165  0.1864
#>   weight sum         76      63
#>   precision      0.0063  0.0063
#> 
#> V42
#>   mean           0.3084  0.2579
#>   std. dev.        0.16  0.1802
#>   weight sum         76      63
#>   precision      0.0057  0.0057
#> 
#> V43
#>   mean           0.2817  0.2138
#>   std. dev.      0.1473  0.1374
#>   weight sum         76      63
#>   precision      0.0057  0.0057
#> 
#> V44
#>   mean           0.2534  0.1773
#>   std. dev.      0.1506  0.1163
#>   weight sum         76      63
#>   precision      0.0058  0.0058
#> 
#> V45
#>   mean           0.2519  0.1489
#>   std. dev.      0.1718  0.1048
#>   weight sum         76      63
#>   precision      0.0051  0.0051
#> 
#> V46
#>   mean           0.2029   0.124
#>   std. dev.      0.1501  0.1048
#>   weight sum         76      63
#>   precision      0.0054  0.0054
#> 
#> V47
#>   mean           0.1498  0.1002
#>   std. dev.       0.104  0.0782
#>   weight sum         76      63
#>   precision      0.0041  0.0041
#> 
#> V48
#>   mean           0.1126   0.073
#>   std. dev.      0.0745  0.0533
#>   weight sum         76      63
#>   precision      0.0025  0.0025
#> 
#> V49
#>   mean           0.0663  0.0398
#>   std. dev.      0.0406  0.0338
#>   weight sum         76      63
#>   precision      0.0015  0.0015
#> 
#> V5
#>   mean           0.0924  0.0684
#>   std. dev.      0.0659  0.0492
#>   weight sum         76      63
#>   precision       0.003   0.003
#> 
#> V50
#>   mean           0.0239  0.0187
#>   std. dev.      0.0159  0.0138
#>   weight sum         76      63
#>   precision      0.0007  0.0007
#> 
#> V51
#>   mean           0.0196  0.0134
#>   std. dev.      0.0147  0.0096
#>   weight sum         76      63
#>   precision      0.0008  0.0008
#> 
#> V52
#>   mean           0.0156  0.0106
#>   std. dev.      0.0106  0.0077
#>   weight sum         76      63
#>   precision      0.0006  0.0006
#> 
#> V53
#>   mean           0.0114  0.0093
#>   std. dev.      0.0079   0.006
#>   weight sum         76      63
#>   precision      0.0004  0.0004
#> 
#> V54
#>   mean           0.0122  0.0095
#>   std. dev.      0.0089  0.0056
#>   weight sum         76      63
#>   precision      0.0003  0.0003
#> 
#> V55
#>   mean           0.0107  0.0083
#>   std. dev.      0.0092  0.0052
#>   weight sum         76      63
#>   precision      0.0004  0.0004
#> 
#> V56
#>   mean           0.0087  0.0074
#>   std. dev.      0.0065  0.0046
#>   weight sum         76      63
#>   precision      0.0004  0.0004
#> 
#> V57
#>   mean           0.0081  0.0071
#>   std. dev.      0.0061   0.005
#>   weight sum         76      63
#>   precision      0.0004  0.0004
#> 
#> V58
#>   mean            0.009  0.0062
#>   std. dev.      0.0074  0.0044
#>   weight sum         76      63
#>   precision      0.0005  0.0005
#> 
#> V59
#>   mean           0.0086  0.0067
#>   std. dev.      0.0067  0.0049
#>   weight sum         76      63
#>   precision      0.0004  0.0004
#> 
#> V6
#>   mean           0.1133  0.1045
#>   std. dev.      0.0539  0.0708
#>   weight sum         76      63
#>   precision      0.0028  0.0028
#> 
#> V60
#>   mean           0.0061  0.0062
#>   std. dev.      0.0045  0.0038
#>   weight sum         76      63
#>   precision      0.0003  0.0003
#> 
#> V7
#>   mean           0.1255  0.1233
#>   std. dev.      0.0541  0.0697
#>   weight sum         76      63
#>   precision      0.0028  0.0028
#> 
#> V8
#>   mean           0.1505  0.1248
#>   std. dev.      0.0744  0.0808
#>   weight sum         76      63
#>   precision      0.0031  0.0031
#> 
#> V9
#>   mean           0.2074  0.1425
#>   std. dev.      0.1053  0.0957
#>   weight sum         76      63
#>   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 
#>  0.3188406