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           0.0382  0.0232
#>   std. dev.      0.0292  0.0142
#>   weight sum         72      67
#>   precision      0.0011  0.0011
#> 
#> V10
#>   mean           0.2629  0.1668
#>   std. dev.      0.1472  0.1078
#>   weight sum         72      67
#>   precision       0.005   0.005
#> 
#> V11
#>   mean           0.3122  0.1814
#>   std. dev.      0.1335  0.1036
#>   weight sum         72      67
#>   precision      0.0052  0.0052
#> 
#> V12
#>   mean           0.3239  0.1909
#>   std. dev.      0.1241  0.1277
#>   weight sum         72      67
#>   precision      0.0046  0.0046
#> 
#> V13
#>   mean           0.3387  0.2278
#>   std. dev.      0.1318  0.1362
#>   weight sum         72      67
#>   precision      0.0051  0.0051
#> 
#> V14
#>   mean           0.3351  0.2773
#>   std. dev.      0.1595  0.1687
#>   weight sum         72      67
#>   precision      0.0071  0.0071
#> 
#> V15
#>   mean           0.3327   0.316
#>   std. dev.      0.1869    0.22
#>   weight sum         72      67
#>   precision      0.0072  0.0072
#> 
#> V16
#>   mean           0.3789  0.3925
#>   std. dev.      0.2071  0.2506
#>   weight sum         72      67
#>   precision      0.0071  0.0071
#> 
#> V17
#>   mean            0.412   0.446
#>   std. dev.      0.2357  0.2861
#>   weight sum         72      67
#>   precision       0.007   0.007
#> 
#> V18
#>   mean           0.4502  0.4679
#>   std. dev.      0.2565  0.2686
#>   weight sum         72      67
#>   precision      0.0071  0.0071
#> 
#> V19
#>   mean           0.5368  0.4735
#>   std. dev.      0.2514  0.2509
#>   weight sum         72      67
#>   precision      0.0069  0.0069
#> 
#> V2
#>   mean           0.0497   0.032
#>   std. dev.      0.0418   0.026
#>   weight sum         72      67
#>   precision      0.0019  0.0019
#> 
#> V20
#>   mean           0.6298  0.5025
#>   std. dev.      0.2423  0.2631
#>   weight sum         72      67
#>   precision      0.0069  0.0069
#> 
#> V21
#>   mean           0.6786  0.5522
#>   std. dev.      0.2435  0.2479
#>   weight sum         72      67
#>   precision      0.0072  0.0072
#> 
#> V22
#>   mean           0.6801  0.5812
#>   std. dev.       0.243  0.2448
#>   weight sum         72      67
#>   precision      0.0067  0.0067
#> 
#> V23
#>   mean           0.6813   0.603
#>   std. dev.      0.2648  0.2458
#>   weight sum         72      67
#>   precision      0.0072  0.0072
#> 
#> V24
#>   mean           0.6915  0.6407
#>   std. dev.      0.2592  0.2336
#>   weight sum         72      67
#>   precision      0.0073  0.0073
#> 
#> V25
#>   mean           0.6921  0.6678
#>   std. dev.      0.2396  0.2369
#>   weight sum         72      67
#>   precision      0.0073  0.0073
#> 
#> V26
#>   mean           0.7185  0.6971
#>   std. dev.      0.2354   0.216
#>   weight sum         72      67
#>   precision       0.007   0.007
#> 
#> V27
#>   mean           0.7187  0.6826
#>   std. dev.      0.2684  0.2149
#>   weight sum         72      67
#>   precision      0.0074  0.0074
#> 
#> V28
#>   mean           0.7163  0.6528
#>   std. dev.      0.2536  0.2044
#>   weight sum         72      67
#>   precision      0.0075  0.0075
#> 
#> V29
#>   mean           0.6366  0.6068
#>   std. dev.      0.2445  0.2444
#>   weight sum         72      67
#>   precision      0.0074  0.0074
#> 
#> V3
#>   mean           0.0556  0.0384
#>   std. dev.      0.0494  0.0313
#>   weight sum         72      67
#>   precision      0.0024  0.0024
#> 
#> V30
#>   mean           0.5625  0.5503
#>   std. dev.      0.2042  0.2465
#>   weight sum         72      67
#>   precision      0.0069  0.0069
#> 
#> V31
#>   mean           0.4809  0.4999
#>   std. dev.      0.2149   0.211
#>   weight sum         72      67
#>   precision      0.0063  0.0063
#> 
#> V32
#>   mean           0.4298  0.4278
#>   std. dev.      0.2226  0.2206
#>   weight sum         72      67
#>   precision      0.0064  0.0064
#> 
#> V33
#>   mean           0.3984  0.4236
#>   std. dev.       0.211  0.2174
#>   weight sum         72      67
#>   precision      0.0069  0.0069
#> 
#> V34
#>   mean           0.3765  0.4504
#>   std. dev.      0.2164  0.2403
#>   weight sum         72      67
#>   precision      0.0068  0.0068
#> 
#> V35
#>   mean           0.3447  0.4706
#>   std. dev.      0.2552  0.2474
#>   weight sum         72      67
#>   precision      0.0072  0.0072
#> 
#> V36
#>   mean            0.328  0.4867
#>   std. dev.      0.2373  0.2543
#>   weight sum         72      67
#>   precision      0.0072  0.0072
#> 
#> V37
#>   mean           0.3187  0.4491
#>   std. dev.      0.2241  0.2501
#>   weight sum         72      67
#>   precision      0.0067  0.0067
#> 
#> V38
#>   mean           0.3378   0.371
#>   std. dev.      0.1886  0.2249
#>   weight sum         72      67
#>   precision       0.007   0.007
#> 
#> V39
#>   mean           0.3488  0.3181
#>   std. dev.      0.1827  0.2168
#>   weight sum         72      67
#>   precision      0.0068  0.0068
#> 
#> V4
#>   mean           0.0701  0.0447
#>   std. dev.      0.0616  0.0337
#>   weight sum         72      67
#>   precision      0.0032  0.0032
#> 
#> V40
#>   mean           0.3082  0.3236
#>   std. dev.       0.163  0.1907
#>   weight sum         72      67
#>   precision      0.0067  0.0067
#> 
#> V41
#>   mean           0.2959  0.2963
#>   std. dev.      0.1678  0.1881
#>   weight sum         72      67
#>   precision      0.0063  0.0063
#> 
#> V42
#>   mean             0.32  0.2572
#>   std. dev.       0.177  0.1776
#>   weight sum         72      67
#>   precision      0.0059  0.0059
#> 
#> V43
#>   mean           0.2962  0.2179
#>   std. dev.      0.1479  0.1414
#>   weight sum         72      67
#>   precision      0.0056  0.0056
#> 
#> V44
#>   mean           0.2653  0.1797
#>   std. dev.       0.151  0.1194
#>   weight sum         72      67
#>   precision      0.0058  0.0058
#> 
#> V45
#>   mean           0.2668  0.1507
#>   std. dev.      0.1855  0.0996
#>   weight sum         72      67
#>   precision      0.0052  0.0052
#> 
#> V46
#>   mean           0.2113  0.1256
#>   std. dev.      0.1647  0.0989
#>   weight sum         72      67
#>   precision      0.0054  0.0054
#> 
#> V47
#>   mean           0.1528  0.0979
#>   std. dev.      0.1025  0.0713
#>   weight sum         72      67
#>   precision      0.0041  0.0041
#> 
#> V48
#>   mean           0.1139  0.0724
#>   std. dev.      0.0704  0.0518
#>   weight sum         72      67
#>   precision      0.0024  0.0024
#> 
#> V49
#>   mean           0.0662  0.0411
#>   std. dev.      0.0388  0.0335
#>   weight sum         72      67
#>   precision      0.0015  0.0015
#> 
#> V5
#>   mean           0.0904  0.0669
#>   std. dev.      0.0641  0.0521
#>   weight sum         72      67
#>   precision       0.003   0.003
#> 
#> V50
#>   mean           0.0243  0.0192
#>   std. dev.      0.0152  0.0126
#>   weight sum         72      67
#>   precision      0.0008  0.0008
#> 
#> V51
#>   mean           0.0199  0.0129
#>   std. dev.      0.0154  0.0094
#>   weight sum         72      67
#>   precision      0.0009  0.0009
#> 
#> V52
#>   mean           0.0172  0.0107
#>   std. dev.      0.0125  0.0076
#>   weight sum         72      67
#>   precision      0.0007  0.0007
#> 
#> V53
#>   mean           0.0125  0.0093
#>   std. dev.      0.0083   0.006
#>   weight sum         72      67
#>   precision      0.0004  0.0004
#> 
#> V54
#>   mean           0.0134  0.0095
#>   std. dev.      0.0086  0.0057
#>   weight sum         72      67
#>   precision      0.0003  0.0003
#> 
#> V55
#>   mean           0.0114  0.0085
#>   std. dev.      0.0091  0.0051
#>   weight sum         72      67
#>   precision      0.0004  0.0004
#> 
#> V56
#>   mean           0.0089  0.0071
#>   std. dev.      0.0063  0.0047
#>   weight sum         72      67
#>   precision      0.0004  0.0004
#> 
#> V57
#>   mean           0.0081  0.0079
#>   std. dev.      0.0063  0.0049
#>   weight sum         72      67
#>   precision      0.0004  0.0004
#> 
#> V58
#>   mean           0.0103   0.007
#>   std. dev.      0.0085   0.005
#>   weight sum         72      67
#>   precision      0.0004  0.0004
#> 
#> V59
#>   mean            0.009  0.0073
#>   std. dev.      0.0067  0.0054
#>   weight sum         72      67
#>   precision      0.0004  0.0004
#> 
#> V6
#>   mean           0.1124  0.1023
#>   std. dev.      0.0534   0.071
#>   weight sum         72      67
#>   precision      0.0028  0.0028
#> 
#> V60
#>   mean           0.0063  0.0062
#>   std. dev.      0.0046  0.0037
#>   weight sum         72      67
#>   precision      0.0003  0.0003
#> 
#> V7
#>   mean           0.1249  0.1165
#>   std. dev.      0.0576  0.0709
#>   weight sum         72      67
#>   precision      0.0028  0.0028
#> 
#> V8
#>   mean           0.1561   0.122
#>   std. dev.      0.0899  0.0827
#>   weight sum         72      67
#>   precision      0.0034  0.0034
#> 
#> V9
#>   mean           0.2214  0.1404
#>   std. dev.      0.1264  0.0962
#>   weight sum         72      67
#>   precision       0.005   0.005
#> 
#> 


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

# Score the predictions
predictions$score()
#> classif.ce 
#>  0.3768116