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
#>                 (0.52)  (0.48)
#> ===============================
#> V1
#>   mean            0.034  0.0206
#>   std. dev.      0.0245  0.0126
#>   weight sum         72      67
#>   precision      0.0011  0.0011
#> 
#> V10
#>   mean           0.2525  0.1621
#>   std. dev.      0.1331   0.126
#>   weight sum         72      67
#>   precision      0.0051  0.0051
#> 
#> V11
#>   mean           0.2888  0.1729
#>   std. dev.      0.1233  0.1267
#>   weight sum         72      67
#>   precision      0.0052  0.0052
#> 
#> V12
#>   mean           0.2975  0.1948
#>   std. dev.      0.1286  0.1458
#>   weight sum         72      67
#>   precision       0.005   0.005
#> 
#> V13
#>   mean           0.3163  0.2352
#>   std. dev.      0.1279  0.1403
#>   weight sum         72      67
#>   precision      0.0051  0.0051
#> 
#> V14
#>   mean            0.327  0.2761
#>   std. dev.      0.1664  0.1705
#>   weight sum         72      67
#>   precision      0.0072  0.0072
#> 
#> V15
#>   mean           0.3412  0.3154
#>   std. dev.      0.1983  0.2246
#>   weight sum         72      67
#>   precision      0.0074  0.0074
#> 
#> V16
#>   mean           0.3881  0.4038
#>   std. dev.      0.2202  0.2661
#>   weight sum         72      67
#>   precision      0.0069  0.0069
#> 
#> V17
#>   mean            0.419  0.4561
#>   std. dev.      0.2442  0.2979
#>   weight sum         72      67
#>   precision      0.0073  0.0073
#> 
#> V18
#>   mean           0.4595  0.4877
#>   std. dev.      0.2574  0.2641
#>   weight sum         72      67
#>   precision       0.007   0.007
#> 
#> V19
#>   mean           0.5354  0.4892
#>   std. dev.      0.2549  0.2605
#>   weight sum         72      67
#>   precision       0.007   0.007
#> 
#> V2
#>   mean           0.0463  0.0305
#>   std. dev.      0.0401  0.0255
#>   weight sum         72      67
#>   precision      0.0018  0.0018
#> 
#> V20
#>   mean           0.6185  0.5195
#>   std. dev.      0.2529  0.2609
#>   weight sum         72      67
#>   precision      0.0068  0.0068
#> 
#> V21
#>   mean           0.6709   0.572
#>   std. dev.      0.2551  0.2495
#>   weight sum         72      67
#>   precision      0.0072  0.0072
#> 
#> V22
#>   mean            0.672  0.6123
#>   std. dev.       0.249  0.2568
#>   weight sum         72      67
#>   precision      0.0068  0.0068
#> 
#> V23
#>   mean           0.6806  0.6422
#>   std. dev.      0.2635  0.2365
#>   weight sum         72      67
#>   precision      0.0071  0.0071
#> 
#> V24
#>   mean           0.6888  0.6673
#>   std. dev.      0.2541  0.2323
#>   weight sum         72      67
#>   precision      0.0075  0.0075
#> 
#> V25
#>   mean           0.6797  0.6741
#>   std. dev.      0.2478  0.2533
#>   weight sum         72      67
#>   precision      0.0072  0.0072
#> 
#> V26
#>   mean           0.7121  0.6892
#>   std. dev.      0.2349  0.2461
#>   weight sum         72      67
#>   precision      0.0066  0.0066
#> 
#> V27
#>   mean           0.7213  0.6854
#>   std. dev.      0.2614  0.2328
#>   weight sum         72      67
#>   precision      0.0071  0.0071
#> 
#> V28
#>   mean           0.7237   0.662
#>   std. dev.       0.254  0.2132
#>   weight sum         72      67
#>   precision      0.0064  0.0064
#> 
#> V29
#>   mean           0.6737  0.6098
#>   std. dev.      0.2348  0.2395
#>   weight sum         72      67
#>   precision      0.0072  0.0072
#> 
#> V3
#>   mean           0.0501  0.0362
#>   std. dev.      0.0461  0.0312
#>   weight sum         72      67
#>   precision      0.0023  0.0023
#> 
#> V30
#>   mean            0.591  0.5741
#>   std. dev.      0.1961  0.2286
#>   weight sum         72      67
#>   precision       0.007   0.007
#> 
#> V31
#>   mean           0.4724  0.5486
#>   std. dev.      0.2109  0.1801
#>   weight sum         72      67
#>   precision      0.0067  0.0067
#> 
#> V32
#>   mean           0.3912  0.4685
#>   std. dev.      0.1933  0.1994
#>   weight sum         72      67
#>   precision      0.0064  0.0064
#> 
#> V33
#>   mean           0.3602  0.4407
#>   std. dev.       0.187  0.2045
#>   weight sum         72      67
#>   precision       0.007   0.007
#> 
#> V34
#>   mean           0.3349  0.4333
#>   std. dev.      0.1997  0.2455
#>   weight sum         72      67
#>   precision      0.0068  0.0068
#> 
#> V35
#>   mean           0.3111  0.4451
#>   std. dev.        0.23  0.2695
#>   weight sum         72      67
#>   precision      0.0072  0.0072
#> 
#> V36
#>   mean           0.2969  0.4579
#>   std. dev.      0.2372  0.2618
#>   weight sum         72      67
#>   precision      0.0072  0.0072
#> 
#> V37
#>   mean           0.2964  0.4208
#>   std. dev.      0.2152  0.2408
#>   weight sum         72      67
#>   precision      0.0067  0.0067
#> 
#> V38
#>   mean           0.3011  0.3589
#>   std. dev.      0.1999  0.2216
#>   weight sum         72      67
#>   precision      0.0066  0.0066
#> 
#> V39
#>   mean           0.3197  0.3243
#>   std. dev.      0.1855  0.2176
#>   weight sum         72      67
#>   precision       0.007   0.007
#> 
#> V4
#>   mean           0.0632   0.044
#>   std. dev.      0.0569  0.0338
#>   weight sum         72      67
#>   precision      0.0033  0.0033
#> 
#> V40
#>   mean           0.3019  0.3299
#>   std. dev.      0.1666  0.2012
#>   weight sum         72      67
#>   precision      0.0067  0.0067
#> 
#> V41
#>   mean           0.2796  0.2898
#>   std. dev.      0.1596  0.1802
#>   weight sum         72      67
#>   precision      0.0062  0.0062
#> 
#> V42
#>   mean           0.2899  0.2466
#>   std. dev.      0.1592   0.173
#>   weight sum         72      67
#>   precision      0.0059  0.0059
#> 
#> V43
#>   mean             0.26  0.2066
#>   std. dev.      0.1263  0.1385
#>   weight sum         72      67
#>   precision      0.0056  0.0056
#> 
#> V44
#>   mean           0.2257  0.1715
#>   std. dev.      0.1283  0.1084
#>   weight sum         72      67
#>   precision      0.0058  0.0058
#> 
#> V45
#>   mean           0.2358  0.1421
#>   std. dev.      0.1643  0.0975
#>   weight sum         72      67
#>   precision      0.0051  0.0051
#> 
#> V46
#>   mean           0.1949  0.1147
#>   std. dev.      0.1395  0.0963
#>   weight sum         72      67
#>   precision      0.0046  0.0046
#> 
#> V47
#>   mean           0.1483  0.0972
#>   std. dev.      0.0882  0.0717
#>   weight sum         72      67
#>   precision      0.0031  0.0031
#> 
#> V48
#>   mean           0.1104  0.0721
#>   std. dev.      0.0668    0.05
#>   weight sum         72      67
#>   precision      0.0021  0.0021
#> 
#> V49
#>   mean           0.0641  0.0396
#>   std. dev.      0.0366   0.032
#>   weight sum         72      67
#>   precision      0.0015  0.0015
#> 
#> V5
#>   mean           0.0835  0.0659
#>   std. dev.      0.0564  0.0489
#>   weight sum         72      67
#>   precision       0.003   0.003
#> 
#> V50
#>   mean           0.0224  0.0184
#>   std. dev.      0.0142   0.013
#>   weight sum         72      67
#>   precision      0.0007  0.0007
#> 
#> V51
#>   mean           0.0188  0.0124
#>   std. dev.       0.012  0.0082
#>   weight sum         72      67
#>   precision      0.0007  0.0007
#> 
#> V52
#>   mean           0.0157    0.01
#>   std. dev.      0.0098  0.0064
#>   weight sum         72      67
#>   precision      0.0004  0.0004
#> 
#> V53
#>   mean           0.0117  0.0093
#>   std. dev.      0.0076  0.0063
#>   weight sum         72      67
#>   precision      0.0004  0.0004
#> 
#> V54
#>   mean           0.0115  0.0097
#>   std. dev.      0.0082  0.0055
#>   weight sum         72      67
#>   precision      0.0003  0.0003
#> 
#> V55
#>   mean           0.0092  0.0086
#>   std. dev.      0.0072  0.0049
#>   weight sum         72      67
#>   precision      0.0004  0.0004
#> 
#> V56
#>   mean           0.0085  0.0076
#>   std. dev.      0.0057   0.005
#>   weight sum         72      67
#>   precision      0.0004  0.0004
#> 
#> V57
#>   mean           0.0077  0.0075
#>   std. dev.      0.0056  0.0054
#>   weight sum         72      67
#>   precision      0.0003  0.0003
#> 
#> V58
#>   mean           0.0095  0.0062
#>   std. dev.      0.0073  0.0044
#>   weight sum         72      67
#>   precision      0.0004  0.0004
#> 
#> V59
#>   mean           0.0081  0.0071
#>   std. dev.      0.0056  0.0053
#>   weight sum         72      67
#>   precision      0.0003  0.0003
#> 
#> V6
#>   mean           0.1167  0.1004
#>   std. dev.      0.0555  0.0634
#>   weight sum         72      67
#>   precision      0.0022  0.0022
#> 
#> V60
#>   mean           0.0062  0.0062
#>   std. dev.      0.0044  0.0038
#>   weight sum         72      67
#>   precision      0.0003  0.0003
#> 
#> V7
#>   mean           0.1317  0.1154
#>   std. dev.      0.0561  0.0626
#>   weight sum         72      67
#>   precision      0.0022  0.0022
#> 
#> V8
#>   mean           0.1493  0.1194
#>   std. dev.      0.0831  0.0808
#>   weight sum         72      67
#>   precision      0.0033  0.0033
#> 
#> V9
#>   mean           0.2137  0.1356
#>   std. dev.      0.1135  0.1077
#>   weight sum         72      67
#>   precision      0.0048  0.0048
#> 
#> 


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

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