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           0.0386  0.0212
#>   std. dev.      0.0304  0.0139
#>   weight sum         72      67
#>   precision      0.0011  0.0011
#> 
#> V10
#>   mean            0.243  0.1535
#>   std. dev.      0.1421  0.1041
#>   weight sum         72      67
#>   precision       0.005   0.005
#> 
#> V11
#>   mean            0.274  0.1704
#>   std. dev.      0.1158   0.108
#>   weight sum         72      67
#>   precision      0.0051  0.0051
#> 
#> V12
#>   mean           0.2871  0.1843
#>   std. dev.       0.118  0.1342
#>   weight sum         72      67
#>   precision      0.0046  0.0046
#> 
#> V13
#>   mean           0.2892  0.2402
#>   std. dev.      0.1277  0.1423
#>   weight sum         72      67
#>   precision      0.0051  0.0051
#> 
#> V14
#>   mean           0.2957  0.2975
#>   std. dev.       0.144  0.1729
#>   weight sum         72      67
#>   precision      0.0059  0.0059
#> 
#> V15
#>   mean           0.3175  0.3335
#>   std. dev.      0.1763   0.225
#>   weight sum         72      67
#>   precision      0.0073  0.0073
#> 
#> V16
#>   mean           0.3697  0.3973
#>   std. dev.      0.1957  0.2695
#>   weight sum         72      67
#>   precision      0.0072  0.0072
#> 
#> V17
#>   mean           0.3945  0.4392
#>   std. dev.      0.2366  0.2995
#>   weight sum         72      67
#>   precision      0.0073  0.0073
#> 
#> V18
#>   mean           0.4315  0.4645
#>   std. dev.      0.2548  0.2728
#>   weight sum         72      67
#>   precision      0.0068  0.0068
#> 
#> V19
#>   mean           0.5094  0.4655
#>   std. dev.      0.2583   0.263
#>   weight sum         72      67
#>   precision      0.0069  0.0069
#> 
#> V2
#>   mean            0.051  0.0311
#>   std. dev.      0.0419  0.0261
#>   weight sum         72      67
#>   precision      0.0018  0.0018
#> 
#> V20
#>   mean           0.6008  0.4906
#>   std. dev.      0.2661  0.2672
#>   weight sum         72      67
#>   precision      0.0069  0.0069
#> 
#> V21
#>   mean            0.652  0.5514
#>   std. dev.      0.2687  0.2587
#>   weight sum         72      67
#>   precision      0.0071  0.0071
#> 
#> V22
#>   mean            0.642  0.5867
#>   std. dev.      0.2488   0.265
#>   weight sum         72      67
#>   precision       0.007   0.007
#> 
#> V23
#>   mean           0.6391  0.6275
#>   std. dev.       0.256   0.254
#>   weight sum         72      67
#>   precision       0.007   0.007
#> 
#> V24
#>   mean           0.6572  0.6724
#>   std. dev.      0.2451  0.2338
#>   weight sum         72      67
#>   precision      0.0074  0.0074
#> 
#> V25
#>   mean           0.6449  0.6909
#>   std. dev.      0.2501  0.2378
#>   weight sum         72      67
#>   precision      0.0073  0.0073
#> 
#> V26
#>   mean           0.6651  0.7126
#>   std. dev.      0.2454  0.2196
#>   weight sum         72      67
#>   precision      0.0069  0.0069
#> 
#> V27
#>   mean           0.6705  0.6815
#>   std. dev.      0.2815  0.2174
#>   weight sum         72      67
#>   precision      0.0075  0.0075
#> 
#> V28
#>   mean           0.6825   0.649
#>   std. dev.      0.2735  0.2062
#>   weight sum         72      67
#>   precision      0.0075  0.0075
#> 
#> V29
#>   mean           0.6379  0.6109
#>   std. dev.      0.2524  0.2389
#>   weight sum         72      67
#>   precision      0.0075  0.0075
#> 
#> V3
#>   mean           0.0546  0.0363
#>   std. dev.      0.0502    0.03
#>   weight sum         72      67
#>   precision      0.0023  0.0023
#> 
#> V30
#>   mean           0.5983  0.5679
#>   std. dev.      0.2186  0.2334
#>   weight sum         72      67
#>   precision       0.007   0.007
#> 
#> V31
#>   mean           0.5031  0.5173
#>   std. dev.      0.2253  0.1987
#>   weight sum         72      67
#>   precision      0.0063  0.0063
#> 
#> V32
#>   mean           0.4425  0.4375
#>   std. dev.      0.2191    0.22
#>   weight sum         72      67
#>   precision      0.0065  0.0065
#> 
#> V33
#>   mean           0.4191  0.4252
#>   std. dev.      0.1951  0.2325
#>   weight sum         72      67
#>   precision      0.0069  0.0069
#> 
#> V34
#>   mean           0.3959   0.425
#>   std. dev.      0.2131  0.2443
#>   weight sum         72      67
#>   precision      0.0067  0.0067
#> 
#> V35
#>   mean           0.3702  0.4442
#>   std. dev.      0.2568  0.2573
#>   weight sum         72      67
#>   precision      0.0072  0.0072
#> 
#> V36
#>   mean           0.3452  0.4553
#>   std. dev.      0.2565  0.2621
#>   weight sum         72      67
#>   precision      0.0072  0.0072
#> 
#> V37
#>   mean           0.3465  0.4092
#>   std. dev.       0.238  0.2516
#>   weight sum         72      67
#>   precision      0.0066  0.0066
#> 
#> V38
#>   mean           0.3714  0.3299
#>   std. dev.      0.2182  0.2255
#>   weight sum         72      67
#>   precision       0.007   0.007
#> 
#> V39
#>   mean           0.3648  0.2922
#>   std. dev.      0.1944  0.2156
#>   weight sum         72      67
#>   precision      0.0068  0.0068
#> 
#> V4
#>   mean           0.0706  0.0429
#>   std. dev.      0.0627  0.0313
#>   weight sum         72      67
#>   precision      0.0034  0.0034
#> 
#> V40
#>   mean           0.3217  0.3088
#>   std. dev.      0.1769  0.1906
#>   weight sum         72      67
#>   precision      0.0067  0.0067
#> 
#> V41
#>   mean           0.3137  0.2874
#>   std. dev.      0.1692  0.1787
#>   weight sum         72      67
#>   precision      0.0063  0.0063
#> 
#> V42
#>   mean           0.3184  0.2461
#>   std. dev.      0.1674  0.1795
#>   weight sum         72      67
#>   precision      0.0057  0.0057
#> 
#> V43
#>   mean           0.2759  0.2096
#>   std. dev.      0.1453   0.143
#>   weight sum         72      67
#>   precision      0.0057  0.0057
#> 
#> V44
#>   mean            0.242   0.174
#>   std. dev.      0.1479  0.1149
#>   weight sum         72      67
#>   precision      0.0058  0.0058
#> 
#> V45
#>   mean           0.2497  0.1423
#>   std. dev.      0.1742  0.1002
#>   weight sum         72      67
#>   precision      0.0045  0.0045
#> 
#> V46
#>   mean           0.2024  0.1134
#>   std. dev.      0.1521  0.0961
#>   weight sum         72      67
#>   precision      0.0055  0.0055
#> 
#> V47
#>   mean           0.1484  0.0911
#>   std. dev.      0.1009   0.071
#>   weight sum         72      67
#>   precision      0.0041  0.0041
#> 
#> V48
#>   mean           0.1174  0.0714
#>   std. dev.      0.0691  0.0522
#>   weight sum         72      67
#>   precision      0.0024  0.0024
#> 
#> V49
#>   mean           0.0685  0.0398
#>   std. dev.       0.037  0.0341
#>   weight sum         72      67
#>   precision      0.0015  0.0015
#> 
#> V5
#>   mean           0.0904  0.0599
#>   std. dev.      0.0647  0.0462
#>   weight sum         72      67
#>   precision       0.003   0.003
#> 
#> V50
#>   mean           0.0239  0.0169
#>   std. dev.      0.0141  0.0123
#>   weight sum         72      67
#>   precision      0.0007  0.0007
#> 
#> V51
#>   mean           0.0203  0.0129
#>   std. dev.      0.0148  0.0084
#>   weight sum         72      67
#>   precision      0.0009  0.0009
#> 
#> V52
#>   mean            0.017  0.0102
#>   std. dev.      0.0114  0.0068
#>   weight sum         72      67
#>   precision      0.0006  0.0006
#> 
#> V53
#>   mean           0.0116  0.0095
#>   std. dev.      0.0074  0.0059
#>   weight sum         72      67
#>   precision      0.0004  0.0004
#> 
#> V54
#>   mean           0.0126  0.0094
#>   std. dev.      0.0093  0.0055
#>   weight sum         72      67
#>   precision      0.0003  0.0003
#> 
#> V55
#>   mean           0.0106  0.0082
#>   std. dev.       0.008   0.005
#>   weight sum         72      67
#>   precision      0.0004  0.0004
#> 
#> V56
#>   mean           0.0092  0.0077
#>   std. dev.      0.0061  0.0048
#>   weight sum         72      67
#>   precision      0.0003  0.0003
#> 
#> V57
#>   mean           0.0078  0.0078
#>   std. dev.      0.0052  0.0055
#>   weight sum         72      67
#>   precision      0.0003  0.0003
#> 
#> V58
#>   mean           0.0084  0.0068
#>   std. dev.      0.0055  0.0048
#>   weight sum         72      67
#>   precision      0.0002  0.0002
#> 
#> V59
#>   mean            0.009  0.0071
#>   std. dev.      0.0076  0.0047
#>   weight sum         72      67
#>   precision      0.0004  0.0004
#> 
#> V6
#>   mean           0.1074  0.0962
#>   std. dev.      0.0547  0.0647
#>   weight sum         72      67
#>   precision      0.0028  0.0028
#> 
#> V60
#>   mean           0.0071  0.0061
#>   std. dev.      0.0068   0.004
#>   weight sum         72      67
#>   precision      0.0005  0.0005
#> 
#> V7
#>   mean           0.1215   0.116
#>   std. dev.      0.0577  0.0668
#>   weight sum         72      67
#>   precision      0.0028  0.0028
#> 
#> V8
#>   mean           0.1542  0.1195
#>   std. dev.      0.0918  0.0826
#>   weight sum         72      67
#>   precision      0.0034  0.0034
#> 
#> V9
#>   mean           0.2117  0.1318
#>   std. dev.      0.1339  0.0911
#>   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